From 77ee03f16e7663c546fedc7ff0e43fa5b75adb0b Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Mon, 29 Jun 2020 09:14:21 -0400 Subject: [PATCH 01/33] version updated for new release --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 71d6a28d4..b49a5ad58 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -13,7 +13,7 @@ SET( ${PROJECT_NAME}_Variant "Full" ) # the particular variant of CaPTk (Full/Ne SET( PROJECT_VERSION_MAJOR 1 ) SET( PROJECT_VERSION_MINOR 8 ) -SET( PROJECT_VERSION_PATCH 0.nonRelease ) +SET( PROJECT_VERSION_PATCH 0.Alpha2 ) SET( PROJECT_VERSION_TWEAK ) # check for the string "nonRelease" in the PROJECT_VERSION_PATCH variable From eb90da508ac4b19ad0f8151c755b44d06e7336b5 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Mon, 29 Jun 2020 09:14:29 -0400 Subject: [PATCH 02/33] html updates --- docs/1_credits.html | 2 +- docs/BreastCancer_LIBRA.html | 2 +- docs/BreastCancer_breastSegmentation.html | 2 +- docs/BreastCancer_texture.html | 2 +- docs/Diffusion_Derivatives.html | 2 +- docs/Download.html | 2 +- docs/FAQ.html | 2 +- docs/Getting_Started.html | 2 +- docs/Glioblastoma_Atlas.html | 2 +- docs/Glioblastoma_Confetti.html | 2 +- docs/Glioblastoma_Directionality.html | 2 +- docs/Glioblastoma_EGFRvIII.html | 2 +- docs/Glioblastoma_Molecular.html | 2 +- docs/Glioblastoma_PHI.html | 2 +- docs/Glioblastoma_Pseudoprogression.html | 2 +- docs/Glioblastoma_Recurrence.html | 2 +- docs/Glioblastoma_Survival.html | 2 +- docs/Glioblastoma_WhiteStripe.html | 2 +- docs/How_To_Guides.html | 2 +- docs/Installation.html | 2 +- docs/LungCancer_SBRT.html | 2 +- docs/PCA_Extraction.html | 2 +- docs/People.html | 2 +- docs/Perfusion_Derivatives.html | 2 +- docs/ReleaseNotes.html | 2 +- docs/Science.html | 2 +- docs/Technical_Reference.html | 2 +- docs/Training_Module.html | 2 +- docs/ht_FeatureExtraction.html | 2 +- docs/ht_Preprocessing.html | 2 +- docs/ht_Segmentation.html | 2 +- docs/ht_SpecialApps.html | 2 +- docs/ht_utilities.html | 2 +- docs/index.html | 2 +- docs/index.qhp | 2 +- docs/pages.html | 2 +- docs/preprocessing_bias.html | 2 +- docs/preprocessing_brats.html | 2 +- docs/preprocessing_dcm2nii.html | 2 +- docs/preprocessing_histoMatch.html | 2 +- docs/preprocessing_reg.html | 2 +- docs/preprocessing_susan.html | 2 +- docs/preprocessing_zScoreNorm.html | 2 +- docs/seg_DL.html | 2 +- docs/seg_GeoTrain.html | 2 +- docs/seg_Geodesic.html | 2 +- docs/seg_SNAP.html | 2 +- docs/tr_FeatureExtraction.html | 2 +- docs/tr_integration.html | 2 +- 49 files changed, 49 insertions(+), 49 deletions(-) diff --git a/docs/1_credits.html b/docs/1_credits.html index 96e51ebc6..36f56b93d 100644 --- a/docs/1_credits.html +++ b/docs/1_credits.html @@ -12,7 +12,7 @@

- Cancer Imaging Phenomics Toolkit (CaPTk) v.1.8.0.nonRelease.20200626.013f692f  | Contact: software@cbica.upenn.edu + Cancer Imaging Phenomics Toolkit (CaPTk) v.1.8.0.Alpha2  | Contact: software@cbica.upenn.edu
Disclaimer: CaPTk has been designed for non-commercial research purposes only and has not been reviewed or approved by the Food and Drug Administration (FDA). It is not intended or recommended for clinical application.
diff --git a/docs/BreastCancer_LIBRA.html b/docs/BreastCancer_LIBRA.html index 36bc3a402..93c8d50f7 100644 --- a/docs/BreastCancer_LIBRA.html +++ b/docs/BreastCancer_LIBRA.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/BreastCancer_breastSegmentation.html b/docs/BreastCancer_breastSegmentation.html index 26c8fd81a..bea629375 100644 --- a/docs/BreastCancer_breastSegmentation.html +++ b/docs/BreastCancer_breastSegmentation.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/BreastCancer_texture.html b/docs/BreastCancer_texture.html index 58db536b1..f2190308a 100644 --- a/docs/BreastCancer_texture.html +++ b/docs/BreastCancer_texture.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Diffusion_Derivatives.html b/docs/Diffusion_Derivatives.html index b13900d00..0484cdb03 100644 --- a/docs/Diffusion_Derivatives.html +++ b/docs/Diffusion_Derivatives.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Download.html b/docs/Download.html index 4d18c0e9a..278035e13 100644 --- a/docs/Download.html +++ b/docs/Download.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/FAQ.html b/docs/FAQ.html index 8fd40b71c..a423ba61e 100644 --- a/docs/FAQ.html +++ b/docs/FAQ.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Getting_Started.html b/docs/Getting_Started.html index f1192d393..d9ec4d340 100644 --- a/docs/Getting_Started.html +++ b/docs/Getting_Started.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Glioblastoma_Atlas.html b/docs/Glioblastoma_Atlas.html index af1c3c8e2..fe839f154 100644 --- a/docs/Glioblastoma_Atlas.html +++ b/docs/Glioblastoma_Atlas.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Glioblastoma_Confetti.html b/docs/Glioblastoma_Confetti.html index 888fadd9d..722b384ff 100644 --- a/docs/Glioblastoma_Confetti.html +++ b/docs/Glioblastoma_Confetti.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Glioblastoma_Directionality.html b/docs/Glioblastoma_Directionality.html index d12dfbadd..a2424e652 100644 --- a/docs/Glioblastoma_Directionality.html +++ b/docs/Glioblastoma_Directionality.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Glioblastoma_EGFRvIII.html b/docs/Glioblastoma_EGFRvIII.html index a6861e368..1b0837b59 100644 --- a/docs/Glioblastoma_EGFRvIII.html +++ b/docs/Glioblastoma_EGFRvIII.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Glioblastoma_Molecular.html b/docs/Glioblastoma_Molecular.html index 35a0b9e1c..f3d806f17 100644 --- a/docs/Glioblastoma_Molecular.html +++ b/docs/Glioblastoma_Molecular.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Glioblastoma_PHI.html b/docs/Glioblastoma_PHI.html index 00d730b16..b7a162abf 100644 --- a/docs/Glioblastoma_PHI.html +++ b/docs/Glioblastoma_PHI.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Glioblastoma_Pseudoprogression.html b/docs/Glioblastoma_Pseudoprogression.html index 3d8db1685..8e5ef116c 100644 --- a/docs/Glioblastoma_Pseudoprogression.html +++ b/docs/Glioblastoma_Pseudoprogression.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Glioblastoma_Recurrence.html b/docs/Glioblastoma_Recurrence.html index 6f934f142..bff93079f 100644 --- a/docs/Glioblastoma_Recurrence.html +++ b/docs/Glioblastoma_Recurrence.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Glioblastoma_Survival.html b/docs/Glioblastoma_Survival.html index 3b1e0a605..7098edb47 100644 --- a/docs/Glioblastoma_Survival.html +++ b/docs/Glioblastoma_Survival.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Glioblastoma_WhiteStripe.html b/docs/Glioblastoma_WhiteStripe.html index f9cbacfe1..380a63483 100644 --- a/docs/Glioblastoma_WhiteStripe.html +++ b/docs/Glioblastoma_WhiteStripe.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/How_To_Guides.html b/docs/How_To_Guides.html index 5c353858f..31fec5efe 100644 --- a/docs/How_To_Guides.html +++ b/docs/How_To_Guides.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Installation.html b/docs/Installation.html index 8a4b6ddbe..43dd9a963 100644 --- a/docs/Installation.html +++ b/docs/Installation.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/LungCancer_SBRT.html b/docs/LungCancer_SBRT.html index 82af3d2da..032b72f59 100644 --- a/docs/LungCancer_SBRT.html +++ b/docs/LungCancer_SBRT.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/PCA_Extraction.html b/docs/PCA_Extraction.html index 4c32390ac..2074ffa86 100644 --- a/docs/PCA_Extraction.html +++ b/docs/PCA_Extraction.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/People.html b/docs/People.html index 04304a1c9..c7f7471db 100644 --- a/docs/People.html +++ b/docs/People.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Perfusion_Derivatives.html b/docs/Perfusion_Derivatives.html index a484e01c9..51de2b8c7 100644 --- a/docs/Perfusion_Derivatives.html +++ b/docs/Perfusion_Derivatives.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/ReleaseNotes.html b/docs/ReleaseNotes.html index 5b5215f5e..e9308023e 100644 --- a/docs/ReleaseNotes.html +++ b/docs/ReleaseNotes.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Science.html b/docs/Science.html index 00f33d66a..f563b65f5 100644 --- a/docs/Science.html +++ b/docs/Science.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Technical_Reference.html b/docs/Technical_Reference.html index f6f43ea79..b1fcf9179 100644 --- a/docs/Technical_Reference.html +++ b/docs/Technical_Reference.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/Training_Module.html b/docs/Training_Module.html index 8277d595f..9b673186f 100644 --- a/docs/Training_Module.html +++ b/docs/Training_Module.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/ht_FeatureExtraction.html b/docs/ht_FeatureExtraction.html index 2247b7eb7..1f462c49f 100644 --- a/docs/ht_FeatureExtraction.html +++ b/docs/ht_FeatureExtraction.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/ht_Preprocessing.html b/docs/ht_Preprocessing.html index 29fbd9e1d..953cf0c94 100644 --- a/docs/ht_Preprocessing.html +++ b/docs/ht_Preprocessing.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/ht_Segmentation.html b/docs/ht_Segmentation.html index c04fea32f..3fc587d79 100644 --- a/docs/ht_Segmentation.html +++ b/docs/ht_Segmentation.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/ht_SpecialApps.html b/docs/ht_SpecialApps.html index 2b8efe5de..e8882ed44 100644 --- a/docs/ht_SpecialApps.html +++ b/docs/ht_SpecialApps.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/ht_utilities.html b/docs/ht_utilities.html index 8eb87ba54..ab9b6ffcf 100644 --- a/docs/ht_utilities.html +++ b/docs/ht_utilities.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/index.html b/docs/index.html index f36f6a1c9..3a9a204e9 100644 --- a/docs/index.html +++ b/docs/index.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/index.qhp b/docs/index.qhp index 1228d70a2..7d40e719f 100644 --- a/docs/index.qhp +++ b/docs/index.qhp @@ -5,7 +5,7 @@ doxygen -
+
diff --git a/docs/pages.html b/docs/pages.html index 1b7f255d2..a67e3dca3 100644 --- a/docs/pages.html +++ b/docs/pages.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/preprocessing_bias.html b/docs/preprocessing_bias.html index 1f60f4934..d4dae3286 100644 --- a/docs/preprocessing_bias.html +++ b/docs/preprocessing_bias.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/preprocessing_brats.html b/docs/preprocessing_brats.html index 3034fef12..aff4097e4 100644 --- a/docs/preprocessing_brats.html +++ b/docs/preprocessing_brats.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/preprocessing_dcm2nii.html b/docs/preprocessing_dcm2nii.html index b9f10f20c..7af811d38 100644 --- a/docs/preprocessing_dcm2nii.html +++ b/docs/preprocessing_dcm2nii.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/preprocessing_histoMatch.html b/docs/preprocessing_histoMatch.html index 6892fee7d..774d33ea6 100644 --- a/docs/preprocessing_histoMatch.html +++ b/docs/preprocessing_histoMatch.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/preprocessing_reg.html b/docs/preprocessing_reg.html index c2233520b..e6a182e41 100644 --- a/docs/preprocessing_reg.html +++ b/docs/preprocessing_reg.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/preprocessing_susan.html b/docs/preprocessing_susan.html index fc2ee2074..7e532b838 100644 --- a/docs/preprocessing_susan.html +++ b/docs/preprocessing_susan.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/preprocessing_zScoreNorm.html b/docs/preprocessing_zScoreNorm.html index ef8f557f3..b3e5fe515 100644 --- a/docs/preprocessing_zScoreNorm.html +++ b/docs/preprocessing_zScoreNorm.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/seg_DL.html b/docs/seg_DL.html index c8f7832b0..c5b650a63 100644 --- a/docs/seg_DL.html +++ b/docs/seg_DL.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/seg_GeoTrain.html b/docs/seg_GeoTrain.html index bde8efaf1..c528f8adb 100644 --- a/docs/seg_GeoTrain.html +++ b/docs/seg_GeoTrain.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/seg_Geodesic.html b/docs/seg_Geodesic.html index 22ee3d39e..d83a67f23 100644 --- a/docs/seg_Geodesic.html +++ b/docs/seg_Geodesic.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/seg_SNAP.html b/docs/seg_SNAP.html index 322f0be10..735ebca03 100644 --- a/docs/seg_SNAP.html +++ b/docs/seg_SNAP.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/tr_FeatureExtraction.html b/docs/tr_FeatureExtraction.html index 822aa7673..106b40b78 100644 --- a/docs/tr_FeatureExtraction.html +++ b/docs/tr_FeatureExtraction.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
diff --git a/docs/tr_integration.html b/docs/tr_integration.html index cca96a858..923fe1785 100644 --- a/docs/tr_integration.html +++ b/docs/tr_integration.html @@ -30,7 +30,7 @@
Cancer Imaging Phenomics Toolkit (CaPTk) -  1.8.0.nonRelease.20200626.013f692f +  1.8.0.Alpha2
From 7ae57d0a115f9a5eac0cb104e89cf5850645cd7e Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Mon, 29 Jun 2020 09:26:53 -0400 Subject: [PATCH 03/33] full utilities list in getting started --- 2_GettingStarted.txt | 36 +++++++++++++++++++++++++----------- 1 file changed, 25 insertions(+), 11 deletions(-) diff --git a/2_GettingStarted.txt b/2_GettingStarted.txt index f3b9c0d17..6c99454d1 100644 --- a/2_GettingStarted.txt +++ b/2_GettingStarted.txt @@ -245,19 +245,33 @@ These are the keyboard shortcuts available on CaPTk: \section gs_utilities Utilities (Command-line only) -To make pipeline construction using CaPTk easier, a bunch of utilities have been provided. They include +To make pipeline construction using CaPTk easier, a bunch of utilities have been provided: + -# Resizing --# DICOM conversion --# Sanity checking --# Image header information +-# Resampling +-# Image information -# Unique values in image --# Changing pixel values. - -For full details, run the command: - -\verbatim -Utilities.exe -u -\endverbatim +-# Get smallest bounding box in mask (optional isotropic bounding box) +-# Test 2 images +-# Create mask from threshold +-# DICOM to NIfTI conversion +-# NIfTI to DICOM & DICOM-Seg conversion +-# Re-orient image +-# Cast image to another pixel type +-# Thresholding: + - Below + - Above + - Above & Below + - Otsu + - Binary +-# Convert file formats +-# Extract Image Series from Joined stack and vice-versa (4D <-> 3D) +-# Transform coordinates from world to image and vice-versa +-# Label similarity Metrics between 2 label images: pure mathematical formulations are given as output +-# BraTS similarity Metrics between 2 label images: special considerations for metrics are done in this mode because output needs to be BraTS-compliant. +-# Collect information from all images in a directory and put it in CSV + +For a full list of this functionality and more details, please see the corresponding [How-To page](ht_utilities.html). -------- From adfd9c0efc099c73411156b9d8cd93079b103979 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Mon, 29 Jun 2020 09:28:12 -0400 Subject: [PATCH 04/33] preprocessing -> pre-processing --- 3_HowToGuides.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/3_HowToGuides.txt b/3_HowToGuides.txt index 65d7bdfe2..745482da9 100644 --- a/3_HowToGuides.txt +++ b/3_HowToGuides.txt @@ -12,7 +12,7 @@ This section provides step-by-step guidance to apply the CaPTk functionalities: - [N4 Bias Correction (ITK filter)](preprocessing_bias.html) - [Histogram Matching](preprocessing_histoMatch.html) - [Z-Score Normalization](preprocessing_zScoreNorm.html) - - [BraTS Preprocessing Pipeline](preprocessing_brats.html) + - [BraTS Pre-processing Pipeline](preprocessing_brats.html) - \subpage ht_Segmentation "Segmentation" - [Interactive Machine Learning based - Utilizing Geodesic Distance Transform and SVM](seg_GeoTrain.html) - [ITK-SNAP](seg_SNAP.html) @@ -58,7 +58,7 @@ The following applications can be called from the GUI: - \subpage preprocessing_bias "Bias Correction (ITK filter)" - \subpage preprocessing_histoMatch "Histogram Matching" - \subpage preprocessing_zScoreNorm "Z-Score Normalization" -- \subpage preprocessing_brats "BraTS Preprocessing Pipeline" +- \subpage preprocessing_brats "BraTS Pre-processing Pipeline" For a full list of available applications and examples, please use the command: @@ -233,7 +233,7 @@ Reference: */ /** -\page preprocessing_brats BraTS Preprocessing Pipeline +\page preprocessing_brats BraTS Pre-processing Pipeline REQUIREMENTS: -# 4 structural MRI images (T1, T1CE, T2, FLAIR) From 1d801020d8b63a1a2967d395d4aba616db989d95 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Mon, 29 Jun 2020 09:28:20 -0400 Subject: [PATCH 05/33] added brats pipeline --- 2_GettingStarted.txt | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/2_GettingStarted.txt b/2_GettingStarted.txt index 6c99454d1..5bbab37b0 100644 --- a/2_GettingStarted.txt +++ b/2_GettingStarted.txt @@ -278,14 +278,15 @@ For a full list of this functionality and more details, please see the correspon \section gs_preprocessing Pre-processing Image pre-processing is essential to quantitative image analysis. CaPTk pre-processing tools available under the "Preprocessing" menu are fully-parameterizable and comprise: - -Denoising. Intensity noise reduction in regions of uniform intensity profile is offered through a low-level image processing method, namely Smallest Univalue Segment Assimilating Nucleus (SUSAN) [1]. This is a custom implementation and does NOT call out to the original implementation distributed by FSL. - -Co-registration. Registration of various images to the same anatomical template, for examining anatomically aligned imaging signals in tandem and at the voxel level, is done using the Greedy Registration algorithm [5]. - -Bias correction. Correction for magnetic field inhomogeneity is provided using a non-parametric non-uniform intensity normalization [2]. - -Intensity normalization. Conversion of signals across modalities to comparable quantities using histogram matching [4]. - -Z-Scoring normalization. Images are normalized using a z-scoring mechanism with option to do the normalization within the region of interest or across the entire image. In addition, there is an option to remove outliers & noise from the image by removing a certain percentage of the top and bottom intensity ranges [6]. - -Histogram Matching - -Skull Stripping (Deep Learning based) - -Mammogram Pre-processing + - Denoising. Intensity noise reduction in regions of uniform intensity profile is offered through a low-level image processing method, namely Smallest Univalue Segment Assimilating Nucleus (SUSAN) [1]. This is a custom implementation and does NOT call out to the original implementation distributed by FSL. + - Co-registration. Registration of various images to the same anatomical template, for examining anatomically aligned imaging signals in tandem and at the voxel level, is done using the Greedy Registration algorithm [5]. + - Bias correction. Correction for magnetic field inhomogeneity is provided using a non-parametric non-uniform intensity normalization [2]. + - Intensity normalization. Conversion of signals across modalities to comparable quantities using histogram matching [4]. + - Z-Scoring normalization. Images are normalized using a z-scoring mechanism with option to do the normalization within the region of interest or across the entire image. In addition, there is an option to remove outliers & noise from the image by removing a certain percentage of the top and bottom intensity ranges [6]. + - Histogram Matching + - Skull Stripping (Deep Learning based) + - Mammogram Pre-processing + - BraTS Pre-processing Pipeline NOTE: An extended set of algorithms are available via the command line utility Preprocessing. For full details, run the command: From 8f1957ffa4e1b215f76ca1363b8ccdf498d87001 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Mon, 29 Jun 2020 09:33:27 -0400 Subject: [PATCH 06/33] updated references --- 2_GettingStarted.txt | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/2_GettingStarted.txt b/2_GettingStarted.txt index 5bbab37b0..2d72e29d0 100644 --- a/2_GettingStarted.txt +++ b/2_GettingStarted.txt @@ -305,10 +305,11 @@ Preprocessing.exe -u References: -# S.M.Smith, J.M.Brady, "SUSAN - a new approach to low level image processing", Int. J. Comput. Vis. 23(1):45-78, 1997. DOI:10.102A :1007963824710 --# N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans MeI maging. 29(6):1310-20, 2010. doi: 10.1109/TMI.2010.2046908 --# S.Bauer, L.P.Nolte, M.Reyes, "Skull-stripping for Tumor-bearing Brain Images", arXiv. abs/1204.0357, 2012. --# L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000D OI:10.1109/42.836373 --# P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI, Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127 +-# N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans MeI maging. 29(6):1310-20, 2010. DOI: 10.1109/TMI.2010.2046908 +-# S.P.Thakur, J.Doshi, S.Pati, S.M.Ha, C.Sako, S.Talbar, U.Kulkarni, C.Davatzikos, G.Erus, S.Bakas, "Brain Extraction on MRI Scans in Presence of Diffuse Glioma: +Multi-institutional Performance Evaluation of Deep Learning Methods and Robust Modality-Agnostic Training", NeuroImage 2020, DOI:10.1016/j.neuroimage.2020.117081 +-# L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000, DOI:10.1109/42.836373 +-# P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI", Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127, DOI:10.1016/j.jalz.2016.06.205 -# T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010. DOI:10.1002/hbm.20906 -------- From 4a5c394255ccb2421555c76dcd8467da801c4b53 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Mon, 29 Jun 2020 09:34:05 -0400 Subject: [PATCH 07/33] html updates --- docs/CaPTk.TAGFILE | 2 +- docs/Getting_Started.html | 58 ++++++++++++++++++++++++++--------- docs/How_To_Guides.html | 2 +- docs/ht_Preprocessing.html | 2 +- docs/ht_Preprocessing.js | 2 +- docs/index.qhp | 4 +-- docs/pages.html | 2 +- docs/preprocessing_brats.html | 4 +-- 8 files changed, 53 insertions(+), 23 deletions(-) diff --git a/docs/CaPTk.TAGFILE b/docs/CaPTk.TAGFILE index fae3b1809..0a84399cc 100644 --- a/docs/CaPTk.TAGFILE +++ b/docs/CaPTk.TAGFILE @@ -79,7 +79,7 @@ preprocessing_brats - BraTS Preprocessing Pipeline + BraTS Pre-processing Pipeline preprocessing_brats.html diff --git a/docs/Getting_Started.html b/docs/Getting_Started.html index d9ec4d340..1372332cf 100644 --- a/docs/Getting_Started.html +++ b/docs/Getting_Started.html @@ -360,21 +360,50 @@

Utilities (Command-line only)

-

To make pipeline construction using CaPTk easier, a bunch of utilities have been provided. They include

    +

    To make pipeline construction using CaPTk easier, a bunch of utilities have been provided:

    +
    1. Resizing
      2. -
    2. DICOM conversion
      3. -
    3. Sanity checking
      4. -
    4. Image header information
      5. +
    5. Resampling
      6. +
    6. Image information
    7. Unique values in image
      8. -
    8. Changing pixel values.
      9. +
    9. Get smallest bounding box in mask (optional isotropic bounding box)
      10. +
    10. Test 2 images
      11. +
    11. Create mask from threshold
      12. +
    12. DICOM to NIfTI conversion
      13. +
    13. NIfTI to DICOM & DICOM-Seg conversion
      14. +
    14. Re-orient image
      15. +
    15. Cast image to another pixel type
      16. +
    16. Thresholding:
        +
      • Below
        • +
      • Above
        • +
      • Above & Below
        • +
      • Otsu
        • +
      • Binary
        • +
      +
      17. +
    17. Convert file formats
      18. +
    18. Extract Image Series from Joined stack and vice-versa (4D <-> 3D)
      19. +
    19. Transform coordinates from world to image and vice-versa
      20. +
    20. Label similarity Metrics between 2 label images: pure mathematical formulations are given as output
      21. +
    21. BraTS similarity Metrics between 2 label images: special considerations for metrics are done in this mode because output needs to be BraTS-compliant.
      22. +
    22. Collect information from all images in a directory and put it in CSV
    -

    For full details, run the command:

    -
    Utilities.exe -u
-
    +

    For a full list of this functionality and more details, please see the corresponding How-To page.

    +

    Pre-processing

    Image pre-processing is essential to quantitative image analysis. CaPTk pre-processing tools available under the "Preprocessing" menu are fully-parameterizable and comprise:

    -

    -Denoising. Intensity noise reduction in regions of uniform intensity profile is offered through a low-level image processing method, namely Smallest Univalue Segment Assimilating Nucleus (SUSAN) [1]. This is a custom implementation and does NOT call out to the original implementation distributed by FSL. -Co-registration. Registration of various images to the same anatomical template, for examining anatomically aligned imaging signals in tandem and at the voxel level, is done using the Greedy Registration algorithm [5]. -Bias correction. Correction for magnetic field inhomogeneity is provided using a non-parametric non-uniform intensity normalization [2]. -Intensity normalization. Conversion of signals across modalities to comparable quantities using histogram matching [4]. -Z-Scoring normalization. Images are normalized using a z-scoring mechanism with option to do the normalization within the region of interest or across the entire image. In addition, there is an option to remove outliers & noise from the image by removing a certain percentage of the top and bottom intensity ranges [6]. -Histogram Matching -Skull Stripping (Deep Learning based) -Mammogram Pre-processing

    +
      +
    • Denoising. Intensity noise reduction in regions of uniform intensity profile is offered through a low-level image processing method, namely Smallest Univalue Segment Assimilating Nucleus (SUSAN) [1]. This is a custom implementation and does NOT call out to the original implementation distributed by FSL.
      • +
    • Co-registration. Registration of various images to the same anatomical template, for examining anatomically aligned imaging signals in tandem and at the voxel level, is done using the Greedy Registration algorithm [5].
      • +
    • Bias correction. Correction for magnetic field inhomogeneity is provided using a non-parametric non-uniform intensity normalization [2].
      • +
    • Intensity normalization. Conversion of signals across modalities to comparable quantities using histogram matching [4].
      • +
    • Z-Scoring normalization. Images are normalized using a z-scoring mechanism with option to do the normalization within the region of interest or across the entire image. In addition, there is an option to remove outliers & noise from the image by removing a certain percentage of the top and bottom intensity ranges [6].
      • +
    • Histogram Matching
      • +
    • Skull Stripping (Deep Learning based)
      • +
    • Mammogram Pre-processing
      • +
    • BraTS Pre-processing Pipeline
      • +

    NOTE: An extended set of algorithms are available via the command line utility Preprocessing. For full details, run the command:

    Preprocessing.exe -u
    @@ -387,11 +416,12 @@

    References:

    1. S.M.Smith, J.M.Brady, "SUSAN - a new approach to low level image processing", Int. J. Comput. Vis. 23(1):45-78, 1997. DOI:10.102A :1007963824710
      2. -
    2. N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans MeI maging. 29(6):1310-20, 2010. doi: 10.1109/TMI.2010.2046908
      3. -
    3. S.Bauer, L.P.Nolte, M.Reyes, "Skull-stripping for Tumor-bearing Brain Images", arXiv. abs/1204.0357, 2012.
      4. -
    4. L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000D OI:10.1109/42.836373
      5. -
    5. P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI, Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127 --# T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010. DOI:10.1002/hbm.20906
      6. +
    6. N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans MeI maging. 29(6):1310-20, 2010. DOI: 10.1109/TMI.2010.2046908
      7. +
    7. S.P.Thakur, J.Doshi, S.Pati, S.M.Ha, C.Sako, S.Talbar, U.Kulkarni, C.Davatzikos, G.Erus, S.Bakas, "Brain Extraction on MRI Scans in Presence of Diffuse Glioma: +Multi-institutional Performance Evaluation of Deep Learning Methods and Robust Modality-Agnostic Training", NeuroImage 2020, DOI:10.1016/j.neuroimage.2020.117081
      8. +
    8. L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000, DOI:10.1109/42.836373
      9. +
    9. P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI", Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127, DOI:10.1016/j.jalz.2016.06.205
      10. +
    10. T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010. DOI:10.1002/hbm.20906

    diff --git a/docs/How_To_Guides.html b/docs/How_To_Guides.html index 31fec5efe..e1ab509b1 100644 --- a/docs/How_To_Guides.html +++ b/docs/How_To_Guides.html @@ -71,7 +71,7 @@
  1. N4 Bias Correction (ITK filter)
  2. Histogram Matching
  3. Z-Score Normalization
    4. -
  4. BraTS Preprocessing Pipeline
    5. +
  5. BraTS Pre-processing Pipeline
  6. Segmentation

    For a full list of available applications and examples, please use the command:

    ${CaPTk_InstallDir}/bin/Preprocessing -h
diff --git a/docs/ht_Preprocessing.js b/docs/ht_Preprocessing.js
index 73665a050..c44957c02 100644
--- a/docs/ht_Preprocessing.js
+++ b/docs/ht_Preprocessing.js
@@ -6,5 +6,5 @@ var ht_Preprocessing =
     [ "Bias Correction (ITK filter)", "preprocessing_bias.html", null ],
     [ "Histogram Matching", "preprocessing_histoMatch.html", null ],
     [ "Z-Scoring Normalization", "preprocessing_zScoreNorm.html", null ],
-    [ "BraTS Preprocessing Pipeline", "preprocessing_brats.html", null ]
+    [ "BraTS Pre-processing Pipeline", "preprocessing_brats.html", null ]
 ];
\ No newline at end of file
diff --git a/docs/index.qhp b/docs/index.qhp
index 7d40e719f..8bda4c962 100644
--- a/docs/index.qhp
+++ b/docs/index.qhp
@@ -55,7 +55,7 @@
           
    
           
    
           
    
-          
    
+          
    
         
    
         
    
           
    
@@ -167,7 +167,7 @@
       
       
       
-      
+      
       
       
       
diff --git a/docs/pages.html b/docs/pages.html
index a67e3dca3..d4d1bf5c9 100644
--- a/docs/pages.html
+++ b/docs/pages.html
@@ -73,7 +73,7 @@
  Bias Correction (ITK filter)
  Histogram Matching
  Z-Scoring Normalization
- BraTS Preprocessing Pipeline
+ BraTS Pre-processing Pipeline
  ▼Segmentation
  Geodesic Training Segmentation
  Geodesic Distance Transform-based Segmentation
diff --git a/docs/preprocessing_brats.html b/docs/preprocessing_brats.html
index aff4097e4..21c66eb97 100644
--- a/docs/preprocessing_brats.html
+++ b/docs/preprocessing_brats.html
@@ -5,7 +5,7 @@
 
 
 
-Cancer Imaging Phenomics Toolkit (CaPTk): BraTS Preprocessing Pipeline
+Cancer Imaging Phenomics Toolkit (CaPTk): BraTS Pre-processing Pipeline
 
 
 
@@ -58,7 +58,7 @@
 
    
 
    
   
    
-
    BraTS Preprocessing Pipeline 
      
    
+
    BraTS Pre-processing Pipeline 
      
    
 
    
 
    
 
    REQUIREMENTS:
      

From 0cfd7f1174316aff84685e36ff491687b157f631 Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:36:47 -0400
Subject: [PATCH 08/33] added docker

---
 2_GettingStarted.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/2_GettingStarted.txt b/2_GettingStarted.txt
index 2d72e29d0..7505ddf38 100644
--- a/2_GettingStarted.txt
+++ b/2_GettingStarted.txt
@@ -388,6 +388,7 @@ Detailed explanation of using the command line is available in the \ref How_To_G
 |     Windows    |                        7, 8, 10                        |        XP, Vista       |
 |      Linux     | Ubuntu 16.04, 18.04; Debian 9, CentOS 7 (source build) | Ubuntu 14.04; CentOS 6 |
 |      macOS     |                      10.13, 10.14                      |          10.12         |
+|     Docker     |                          N.A.                          |           N.A.         |
 
 \section gs_FAQ_2 What DICOM images are currently supported by CaPTk?
   

From 1206681429a22c5f7ea058a2789231f11e19b2d0 Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:36:52 -0400
Subject: [PATCH 09/33] html update

---
 docs/FAQ.html | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/FAQ.html b/docs/FAQ.html
index a423ba61e..81ff184b1 100644
--- a/docs/FAQ.html
+++ b/docs/FAQ.html
@@ -87,6 +87,8 @@
 Linux Ubuntu 16.04, 18.04; Debian 9, CentOS 7 (source build) Ubuntu 14.04; CentOS 6  
 
 macOS 10.13, 10.14 10.12  
+
+Docker N.A. N.A.  
 
 
      
 What DICOM images are currently supported by CaPTk?
      

From 318cf2d1e2531ea59a308447ac3dab9ad605653c Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:37:47 -0400
Subject: [PATCH 10/33] reference update

---
 3_HowToGuides.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3_HowToGuides.txt b/3_HowToGuides.txt
index 745482da9..f2986a0c4 100644
--- a/3_HowToGuides.txt
+++ b/3_HowToGuides.txt
@@ -116,7 +116,7 @@ More usage options are available via the ```greedy``` command-line executable (s
 
 References:
 
--# P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI, Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127
+-# P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI", Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127, DOI:10.1016/j.jalz.2016.06.205
 -# www.github.com/pyushkevich/greedy
 
 --------

From 68a6736c6d9dcec4fc4a8fd86f63dadd53d66c94 Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:38:22 -0400
Subject: [PATCH 11/33] added DOI

---
 3_HowToGuides.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3_HowToGuides.txt b/3_HowToGuides.txt
index f2986a0c4..d6d15f014 100644
--- a/3_HowToGuides.txt
+++ b/3_HowToGuides.txt
@@ -146,7 +146,7 @@ ${CaPTk_InstallDir}/bin/Preprocessing.exe -i C:/test/image.nii.gz -o C:/test/ima
 
 Reference:
 
--# S.M.Smith, J.M.Brady. "SUSAN-A new approach to low level image processing", International Journal of Computer Vision. 23(1):45-78, 1997.
+-# S.M.Smith, J.M.Brady. "SUSAN-A new approach to low level image processing", International Journal of Computer Vision. 23(1):45-78, 1997, DOI:10.1023/A:1007963824710
 
 */
 

From e7b9f1b26830e7dd3615a0d7c68cf94f5f8c5ece Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:39:49 -0400
Subject: [PATCH 12/33] added doi for bias correction

---
 3_HowToGuides.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/3_HowToGuides.txt b/3_HowToGuides.txt
index d6d15f014..9285d2d55 100644
--- a/3_HowToGuides.txt
+++ b/3_HowToGuides.txt
@@ -173,8 +173,8 @@ ${CaPTk_InstallDir}/bin/Preprocessing.exe -i C:/test/image.nii.gz -o C:/test/ima
 
 Reference:
 
--# J.G.Sled, A.P.Zijdenbos, A.C.Evans. "A nonparametric method for automatic correction of intensity nonuniformity in MRI data" IEEE Trans Med Imaging. 17(1):87-97, 1998.
--# N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee. "N4ITK: improved N3 bias correction." IEEE transactions on medical imaging 29.6 (2010): 1310-1320.
+-# J.G.Sled, A.P.Zijdenbos, A.C.Evans. "A nonparametric method for automatic correction of intensity nonuniformity in MRI data", IEEE Trans Med Imaging. 17(1):87-97, 1998, DOI:10.1109/42.668698
+-# N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee. "N4ITK: improved N3 bias correction." IEEE Trans Med Imaging 29(6): 1310-1320, 2010, DOI:10.1109/TMI.2010.2046908
 
 */
 

From 10c61c600b7a7938bf1a2be4d834031cf1677156 Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:40:05 -0400
Subject: [PATCH 13/33] missing "."

---
 3_HowToGuides.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3_HowToGuides.txt b/3_HowToGuides.txt
index 9285d2d55..2d3bd001a 100644
--- a/3_HowToGuides.txt
+++ b/3_HowToGuides.txt
@@ -174,7 +174,7 @@ ${CaPTk_InstallDir}/bin/Preprocessing.exe -i C:/test/image.nii.gz -o C:/test/ima
 Reference:
 
 -# J.G.Sled, A.P.Zijdenbos, A.C.Evans. "A nonparametric method for automatic correction of intensity nonuniformity in MRI data", IEEE Trans Med Imaging. 17(1):87-97, 1998, DOI:10.1109/42.668698
--# N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee. "N4ITK: improved N3 bias correction." IEEE Trans Med Imaging 29(6): 1310-1320, 2010, DOI:10.1109/TMI.2010.2046908
+-# N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee. "N4ITK: improved N3 bias correction." IEEE Trans Med Imaging. 29(6): 1310-1320, 2010, DOI:10.1109/TMI.2010.2046908
 
 */
 

From dc195df98a970016e96596f46f69a6b85be419ee Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:41:02 -0400
Subject: [PATCH 14/33] updated ref

---
 3_HowToGuides.txt | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/3_HowToGuides.txt b/3_HowToGuides.txt
index 2d3bd001a..58d47ab9a 100644
--- a/3_HowToGuides.txt
+++ b/3_HowToGuides.txt
@@ -228,7 +228,7 @@ ${CaPTk_InstallDir}/bin/Preprocessing.exe -i C:/test/input.nii.gz -m C:/test/inp
 
 Reference:
 
--# T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010. DOI:10.1002/hbm.20906
+-# T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010, DOI:10.1002/hbm.20906
 
 */
 
@@ -261,10 +261,10 @@ ${CaPTk_InstallDir}/bin/BraTSPipeline.exe -t1 C:/test/t1.nii.gz -t1c C:/test/t1c
 
 Reference:
 
--# B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694
--# S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI: 10.1038/sdata.2017.117
+-# B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI:10.1109/TMI.2014.2377694
+-# S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI:10.1038/sdata.2017.117
 -# S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge", arXiv preprint arXiv:1811.02629 (2018)
--# T.Rohlfing, N.M.Zahr, E.V.Sullivan,A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human brain mapping, 31(5), pp.798-819. (2010)
+-# T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010, DOI:10.1002/hbm.20906
 
 */
 

From d579d827ea8fba81979b1397499bab55266655a9 Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:41:22 -0400
Subject: [PATCH 15/33] doi call updated

---
 3_HowToGuides.txt | 46 +++++++++++++++++++++++-----------------------
 1 file changed, 23 insertions(+), 23 deletions(-)

diff --git a/3_HowToGuides.txt b/3_HowToGuides.txt
index 58d47ab9a..2ffc00717 100644
--- a/3_HowToGuides.txt
+++ b/3_HowToGuides.txt
@@ -200,7 +200,7 @@ ${CaPTk_InstallDir}/bin/Preprocessing.exe -i C:/test/input.nii.gz -o C:/test/out
 
 Reference:
 
--# L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000. DOI:10.1109/42.836373
+-# L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000, DOI:10.1109/42.836373
 
 */
 
@@ -310,7 +310,7 @@ ${CaPTk_InstallDir}/bin/GeodesicTraining.exe -i C:/inputImage1.nii.gz,C:/inputIm
 
 References:
 
--# B.Gaonkar, L.Shu, G.Hermosillo, Y.Zhan, "Adaptive geodesic transform for segmentation of vertebrae on CT images", Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 9035:16, 2014. DOI:10.1117/12.2043527.
+-# B.Gaonkar, L.Shu, G.Hermosillo, Y.Zhan, "Adaptive geodesic transform for segmentation of vertebrae on CT images", Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 9035:16, 2014, DOI:10.1117/12.2043527.
 
 --------
 
@@ -353,7 +353,7 @@ Within CaPTk specifically, ITK-SNAP is tightly integrated as a tool used for seg
 
 References:
 
--# P.Yushkevich, Y.Gao, G.Gerig, "ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images", Conf Proc IEEE Eng Med Biol Soc. 2016:3342-3345, 2016. DOI:10.1109/EMBC.2016.7591443.
+-# P.Yushkevich, Y.Gao, G.Gerig, "ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images", Conf Proc IEEE Eng Med Biol Soc. 2016:3342-3345, 2016, DOI:10.1109/EMBC.2016.7591443.
 
 --------
 
@@ -390,7 +390,7 @@ References:
 
 -# K.Kamnitsas, C.Ledig, V.F.J.Newcombe, J.P.Simpson, A.D.Kane, D.K.Menon, D.Rueckert, B.Glocker, "Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation", Medical Image Analysis, 2016.
 -# K.Kamnitsas, L.Chen, C.Ledig, D.Rueckert, B.Glocker, "Multi-Scale 3D CNNs for segmentation of brain Lesions in multi-modal MRI", in proceeding of ISLES challenge, MICCAI 2015.
--# S.P.Thakur, J.Doshi, S.Pati, S.M.Ha, C.Sako, S.Talbar, U.Kulkarni, C.Davatzikos, G.Erus, S.Bakas, "Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning", Springer - BrainLes 2019 - LNCS, Vol.11992, 57-68, 2020. DOI: 10.1007/978-3-030-46640-4_6
+-# S.P.Thakur, J.Doshi, S.Pati, S.M.Ha, C.Sako, S.Talbar, U.Kulkarni, C.Davatzikos, G.Erus, S.Bakas, "Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning", Springer - BrainLes 2019 - LNCS, Vol.11992, 57-68, 2020, DOI: 10.1007/978-3-030-46640-4_6
 -# S.P.Thakur, J.Doshi, S.Pati, S.M.Ha, C.Sako, S.Talbar, U.Kulkarni, C.Davatzikos, G.Erus, S.Bakas, "Brain Extraction on MRI Scans in Presence of Diffuse Glioma:
 Multi-institutional Performance Evaluation of Deep Learning Methods and Robust Modality-Agnostic Training", NeuroImage 2020 [Accepted]
 
@@ -534,7 +534,7 @@ ${CaPTk_InstallDir}/bin/MolecularSubtypePredictor.exe -t 1 -i C:/MolecularSubtyp
 
 References:
 
--# L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016. DOI:10.1093/neuonc/nov127.
+-# L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016, DOI:10.1093/neuonc/nov127.
 
 --------
 
@@ -565,7 +565,7 @@ NOTE: WhiteStripe uses the KernelFit library from Lentner (https://github.com/gl
 
 References:
 
--# R.T.Shinohara, E.M.Sweeney, J.Goldsmith, N.Shiee, F.J.Mateen, P.A.Calabresi, S.Jarso, D.L.Pham, D.S.Reich, C.M.Crainiceanu, Australian Imaging Biomarkers Lifestyle Flagship Study of Ageing, Alzheimer's Disease Neuroimaging Initiative. "Statistical normalization techniques for magnetic resonance imaging", Neuroimage Clin. 6:9-19, 2014. DOI:10.1016/j.nicl.2014.08.008
+-# R.T.Shinohara, E.M.Sweeney, J.Goldsmith, N.Shiee, F.J.Mateen, P.A.Calabresi, S.Jarso, D.L.Pham, D.S.Reich, C.M.Crainiceanu, Australian Imaging Biomarkers Lifestyle Flagship Study of Ageing, Alzheimer's Disease Neuroimaging Initiative. "Statistical normalization techniques for magnetic resonance imaging", Neuroimage Clin. 6:9-19, 2014, DOI:10.1016/j.nicl.2014.08.008
 
 --------
 
@@ -607,9 +607,9 @@ ${CaPTk_InstallDir}/bin/EGFRvIIISurrogateIndex.exe -i C:/inputDSCImage.nii.gz -m
 
 References:
 
--# S.Bakas, H.Akbari, J.Pisapia, M.Rozycki, D.M.O'Rourke, C.Davatzikos. "Identification of Imaging Signatures of the Epidermal Growth Factor Receptor Variant III (EGFRvIII) in Glioblastoma", Neuro Oncol. 17(Suppl 5):v154, 2015. DOI:10.1093/neuonc/nov225.05
--# S.Bakas, Z.A.Binder, H.Akbari, M.Martinez-Lage, M.Rozycki, J.J.D.Morrissette, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "Highly-expressed wild-type EGFR and EGFRvIII mutant glioblastomas have similar MRI signature, consistent with deep peritumoral infiltration", Neuro Oncol. 18(Suppl 6):vi125-vi126, 2016. DOI:10.1093/neuonc/now212.523
--# S.Bakas, H.Akbari, J.Pisapia, M.Martinez-Lage, M.Rozycki, S.Rathore, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the phi-index", Clin Cancer Res. 23(16):4724-4734, 2017. DOI:10.1158/1078-0432.CCR-16-1871
+-# S.Bakas, H.Akbari, J.Pisapia, M.Rozycki, D.M.O'Rourke, C.Davatzikos. "Identification of Imaging Signatures of the Epidermal Growth Factor Receptor Variant III (EGFRvIII) in Glioblastoma", Neuro Oncol. 17(Suppl 5):v154, 2015, DOI:10.1093/neuonc/nov225.05
+-# S.Bakas, Z.A.Binder, H.Akbari, M.Martinez-Lage, M.Rozycki, J.J.D.Morrissette, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "Highly-expressed wild-type EGFR and EGFRvIII mutant glioblastomas have similar MRI signature, consistent with deep peritumoral infiltration", Neuro Oncol. 18(Suppl 6):vi125-vi126, 2016, DOI:10.1093/neuonc/now212.523
+-# S.Bakas, H.Akbari, J.Pisapia, M.Martinez-Lage, M.Rozycki, S.Rathore, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the phi-index", Clin Cancer Res. 23(16):4724-4734, 2017, DOI:10.1158/1078-0432.CCR-16-1871
 
 --------
 
@@ -688,9 +688,9 @@ ${CaPTk_InstallDir}/bin/RecurrenceEstimator.exe -t 1 -i C:/RecurrenceSubjects -o
 
 References:
 
--# H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014. DOI:10.1148/radiol.14132458
+-# H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014, DOI:10.1148/radiol.14132458
 -# H.Akbari, L.Macyszyn, J.Pisapia, X.Da, M.Attiah, Y.Bi, S.Pal, R.Davuluri, L.Roccograndi, N.Dahmane, R.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos, "Survival Prediction in Glioblastoma Patients Using Multi-parametric MRI Biomarkers and Machine Learning Methods", American Society of Neuroradiology, O-525:2042-2044, 2015. (http://www.asnr.org/sites/default/files/proceedings/2015_Proceedings.pdf)
--# H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. "Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma", Neurosurgery. 78(4):572-80, 2016. DOI:10.1227/NEU.0000000000001202
+-# H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. "Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma", Neurosurgery. 78(4):572-80, 2016, DOI:10.1227/NEU.0000000000001202
 
 --------
 
@@ -756,7 +756,7 @@ ${CaPTk_InstallDir}/bin/SurvivalPredictor.exe -t 1 -i C:/SurvivalInput -m C:/Sur
 
 References:
 
--# L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016. DOI:10.1093/neuonc/nov127
+-# L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016, DOI:10.1093/neuonc/nov127
 
 --------
 
@@ -929,7 +929,7 @@ ${CaPTk_InstallDir}/bin/PopulationAtlases.exe -i C:/LabelsFile -a C:/AtlasFile -
 
 References:
 
--# M. Bilello, H. Akbari, X. Da, J.M.Pisapia, S.Mohan, R.L.Wolf, D.M.O'Rourke, M.Martinez-Lage, C.Davatzikos. "Population-based MRI atlases of spatial distribution are specific to patient and tumor characteristics in glioblastoma", Neuroimage Clin. 12:34-40, 2016. DOI:10.1016/j.nicl.2016.03.007
+-# M. Bilello, H. Akbari, X. Da, J.M.Pisapia, S.Mohan, R.L.Wolf, D.M.O'Rourke, M.Martinez-Lage, C.Davatzikos. "Population-based MRI atlases of spatial distribution are specific to patient and tumor characteristics in glioblastoma", Neuroimage Clin. 12:34-40, 2016, DOI:10.1016/j.nicl.2016.03.007
 
 --------
 
@@ -979,12 +979,12 @@ ${CaPTk_InstallDir}/bin/Confetti extract -t template/ -f fibers.Bfloat -c cluste
 
 References:
 
--# B.Tunc, M.Ingalhalikar, W.A.Parker, J.Lecoeur, N.Singh, R.L.Wolf, L.Macyszyn, S.Brem, R.Verma, "Individualized Map of White Matter Pathways: Connectivity-based Paradigm for Neurosurgical Planning", Neurosurgery. 79(4):568-77, 2016. DOI:10.1227/NEU.0000000000001183.
--# B.Tunc, W.A.Parker, M.Ingalhalikar, R.Verma, "Automated tract extraction via atlas based Adaptive Clustering", NeuroImage. 102(2):596-607, 2014. DOI:10.1016/j.neuroimage.2014.08.021
+-# B.Tunc, M.Ingalhalikar, W.A.Parker, J.Lecoeur, N.Singh, R.L.Wolf, L.Macyszyn, S.Brem, R.Verma, "Individualized Map of White Matter Pathways: Connectivity-based Paradigm for Neurosurgical Planning", Neurosurgery. 79(4):568-77, 2016, DOI:10.1227/NEU.0000000000001183.
+-# B.Tunc, W.A.Parker, M.Ingalhalikar, R.Verma, "Automated tract extraction via atlas based Adaptive Clustering", NeuroImage. 102(2):596-607, 2014, DOI:10.1016/j.neuroimage.2014.08.021
 -# B.Tunc, A.R.Smith, D.Wasserman, X.Pennec, W.M.Wells, R.Verma, K.M.Pohl, "Multinomial Probabilistic Fiber Representation for Connectivity Driven Clustering", Inf Process Med Imaging. 23:730-41, 2013.
--# B.Fischl, M.I.Sereno, A.M.Dale, "Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system", NeuroImage. 9:195-207, 1999. DOI:10.1006/nimg.1998.0396 
--# R.S.Desikan, F.Segonne, B.Fischl, B.Quinn, B.Dickerson, D.Blacker, R.Buckner, A.Dale, R.Maguire, B.Hyman, M.Albert, R.Killiany, "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest", NeuroImage. 31(3):968-80, 2006. DOI:10.1016/j.neuroimage.2006.01.021
--# M.Jenkinson, C.F.Beckmann, T.E.J.Behrens, M.W.Woolrich, S.M.Smith, "FSL", Neuroimage. 62(2):782-790, 2012. DOI:10.1016/j.neuroimage.2011.09.015
+-# B.Fischl, M.I.Sereno, A.M.Dale, "Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system", NeuroImage. 9:195-207, 1999, DOI:10.1006/nimg.1998.0396 
+-# R.S.Desikan, F.Segonne, B.Fischl, B.Quinn, B.Dickerson, D.Blacker, R.Buckner, A.Dale, R.Maguire, B.Hyman, M.Albert, R.Killiany, "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest", NeuroImage. 31(3):968-80, 2006, DOI:10.1016/j.neuroimage.2006.01.021
+-# M.Jenkinson, C.F.Beckmann, T.E.J.Behrens, M.W.Woolrich, S.M.Smith, "FSL", Neuroimage. 62(2):782-790, 2012, DOI:10.1016/j.neuroimage.2011.09.015
 
 --------
 
@@ -1030,7 +1030,7 @@ The following files are saved in the output folder:
 
 References:
 
--# M.E.Schweitzer, M.A.Stavarache, N.Petersen, S.Bakas, A.J.Tsiouris, C.Davatzikos, M.G.Kaplitt, M.M.Souweidane, "Modulation of Convection Enhanced Delivery (CED) distribution using Focused Ultrasound (FUS)", Neuro Oncol. 19(Suppl 6):vi272, 2017. DOI:10.1093/neuonc/nox168.1118
+-# M.E.Schweitzer, M.A.Stavarache, N.Petersen, S.Bakas, A.J.Tsiouris, C.Davatzikos, M.G.Kaplitt, M.M.Souweidane, "Modulation of Convection Enhanced Delivery (CED) distribution using Focused Ultrasound (FUS)", Neuro Oncol. 19(Suppl 6):vi272, 2017, DOI:10.1093/neuonc/nox168.1118
 
 --------
 */
@@ -1080,7 +1080,7 @@ ${CaPTk_InstallDir}/bin/libra --input C:/inputDICOMDir --output C:/outputDir # l
 
 References:
 
--# B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012. DOI:10.1118/1.4736530
+-# B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012, DOI:10.1118/1.4736530
 
 --------
 
@@ -1110,7 +1110,7 @@ ${CaPTk_InstallDir}/bin/BreastTexturePipeline.exe -i C:/input/image.dcm -o C:/ou
 
 References:
 
--# B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012. DOI:10.1118/1.4736530
+-# B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012, DOI:10.1118/1.4736530
 
 --------
 
@@ -1136,7 +1136,7 @@ This application uses LIBRA [1] to extract the breast region in the loaded image
 
 References:
 
--# B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012. DOI:10.1118/1.4736530
+-# B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012, DOI:10.1118/1.4736530
 
 --------
 */
@@ -1291,7 +1291,7 @@ ${CaPTk_InstallDir}/bin/PerfusionPCA.exe -t 1 -i C:/properly/formatted/inputDir
 
 References:
 
--# H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014. DOI:10.1148/radiol.14132458
+-# H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014, DOI:10.1148/radiol.14132458
 
 --------
 */

From 94e83a109cbe86c69d5e346ec3542b6ded017d80 Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:43:08 -0400
Subject: [PATCH 16/33] updated ref

---
 6.1_Download.txt |  6 +++---
 README.txt       | 15 +++++++++++++++
 2 files changed, 18 insertions(+), 3 deletions(-)

diff --git a/6.1_Download.txt b/6.1_Download.txt
index 15a8ef655..2d178ffe2 100644
--- a/6.1_Download.txt
+++ b/6.1_Download.txt
@@ -11,14 +11,14 @@ Check the [Installation](Installation.html) guide for details on installation.
 --------------------------------------------------------------------
 ### Please make sure that whenever you use and/or refer to CaPTk in your research, you should always cite the following papers:
 
-- C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018. DOI:10.1117/1.JMI.5.1.011018
-- S.Pati, A.Singh, S.Rathore, A.Gastounioti, M.Bergman, P.Ngo, S.M.Ha, D.Bounias, J.Minock, G.Murphy, H.Li, A.Bhattarai, A.Wolf, P.Sridaran, R.Kalarot, H.Akbari, A.Sotiras, S.P.Thakur, R.Verma, R.T.Shinohara, P.Yushkevich, Y.Fan, D.Kontos, C.Davatzikos, S.Bakas, "The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview", Springer - BrainLes 2019 - LNCS, Vol.11993, 380-394, 2020. DOI: 10.1007/978-3-030-46643-5_38
+- C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018, DOI:10.1117/1.JMI.5.1.011018
+- S.Pati, A.Singh, S.Rathore, A.Gastounioti, M.Bergman, P.Ngo, S.M.Ha, D.Bounias, J.Minock, G.Murphy, H.Li, A.Bhattarai, A.Wolf, P.Sridaran, R.Kalarot, H.Akbari, A.Sotiras, S.P.Thakur, R.Verma, R.T.Shinohara, P.Yushkevich, Y.Fan, D.Kontos, C.Davatzikos, S.Bakas, "The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview", Springer - BrainLes 2019 - LNCS, Vol.11993, 380-394, 2020, DOI:10.1007/978-3-030-46643-5_38
 
   
 In addition, if the journal/conference where you submit your paper does not restrict you from citing abstracts you might also cite the following:
 
 - RRID: SCR_017323 
-- S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017. DOI:10.1007/978-3-319-75238-9_12
+- S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017, DOI:10.1007/978-3-319-75238-9_12
 - S.Pati, S.Bakas, A.Sotiras, R.Kalarot, P.Sridharan, M.Bergman, S.Rathore, H.Akbari, P.Yushkevich, T.Shinohara, Y.Fan, D.Kontos, R.Verma, C.Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26-Dec.1, 2017, Chicago IL.
 - S.Pati, S.Rathore, R.Kalarot, P.Sridharan, M.Bergman, T.Shinohara, P.Yushkevich, Y.Fan, R.Verma, D.Kontos, C.Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html
 
diff --git a/README.txt b/README.txt
index 69f8d75fa..4e9f0c471 100644
--- a/README.txt
+++ b/README.txt
@@ -17,6 +17,21 @@ CaPTk is developed and maintained by the Center for Biomedical Image Computing a
 \image html 0_overview_resize.png "Fig.1. Overview of all functions and applications of CaPTk, in its two-level architecture"
 \image latex 0_overview_resize.png "Fig.1. Overview of all functions and applications of CaPTk, in its two-level architecture"
 
+## Citations
+
+Please make sure that whenever you use and/or refer to CaPTk in your research, you should always cite the following papers:
+
+- C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018, DOI:10.1117/1.JMI.5.1.011018
+- S.Pati, A.Singh, S.Rathore, A.Gastounioti, M.Bergman, P.Ngo, S.M.Ha, D.Bounias, J.Minock, G.Murphy, H.Li, A.Bhattarai, A.Wolf, P.Sridaran, R.Kalarot, H.Akbari, A.Sotiras, S.P.Thakur, R.Verma, R.T.Shinohara, P.Yushkevich, Y.Fan, D.Kontos, C.Davatzikos, S.Bakas, "The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview", Springer - BrainLes 2019 - LNCS, Vol.11993, 380-394, 2020, DOI:10.1007/978-3-030-46643-5_38
+  
+In addition, if the journal/conference where you submit your paper does not restrict you from citing abstracts you might also cite the following:
+
+- RRID: SCR_017323 
+- S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017, DOI:10.1007/978-3-319-75238-9_12
+- S.Pati, S.Bakas, A.Sotiras, R.Kalarot, P.Sridharan, M.Bergman, S.Rathore, H.Akbari, P.Yushkevich, T.Shinohara, Y.Fan, D.Kontos, R.Verma, C.Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26-Dec.1, 2017, Chicago IL.
+- S.Pati, S.Rathore, R.Kalarot, P.Sridharan, M.Bergman, T.Shinohara, P.Yushkevich, Y.Fan, R.Verma, D.Kontos, C.Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html
+
+
 ## Bug Tracker and Feature Request
  
 We coordinate our bugs and feature requests via out GitHub page: https://github.com/CBICA/CaPTk/issues

From 9a91239f3da466ae80034995d5f6a47ff751bb50 Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:43:45 -0400
Subject: [PATCH 17/33] placed citations after FAQ

---
 README.txt | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/README.txt b/README.txt
index 4e9f0c471..f8eecfa7f 100644
--- a/README.txt
+++ b/README.txt
@@ -17,6 +17,14 @@ CaPTk is developed and maintained by the Center for Biomedical Image Computing a
 \image html 0_overview_resize.png "Fig.1. Overview of all functions and applications of CaPTk, in its two-level architecture"
 \image latex 0_overview_resize.png "Fig.1. Overview of all functions and applications of CaPTk, in its two-level architecture"
 
+## Bug Tracker and Feature Request
+ 
+We coordinate our bugs and feature requests via out GitHub page: https://github.com/CBICA/CaPTk/issues
+
+## Frequently Asked Questions (FAQ)
+
+Please see our [FAQ Section](gs_FAQ.html).
+
 ## Citations
 
 Please make sure that whenever you use and/or refer to CaPTk in your research, you should always cite the following papers:
@@ -32,14 +40,6 @@ In addition, if the journal/conference where you submit your paper does not rest
 - S.Pati, S.Rathore, R.Kalarot, P.Sridharan, M.Bergman, T.Shinohara, P.Yushkevich, Y.Fan, R.Verma, D.Kontos, C.Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html
 
 
-## Bug Tracker and Feature Request
- 
-We coordinate our bugs and feature requests via out GitHub page: https://github.com/CBICA/CaPTk/issues
-
-## Frequently Asked Questions (FAQ)
-
-Please see our [FAQ Section](gs_FAQ.html).
-
 ## Supporting Grant
 This work is supported by the NIH/NCI/ITCR* grant U24-CA189523.
 
      * National Institutes of Health / National Cancer Institute / Informatics Technology for Cancer Research

From fb2c9e4a2105b015e83f5a8c51f4b3e7b0462765 Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:44:34 -0400
Subject: [PATCH 18/33] doi call standardized

---
 2_GettingStarted.txt | 22 +++++++++++-----------
 4_Science.txt        | 24 ++++++++++++------------
 2 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/2_GettingStarted.txt b/2_GettingStarted.txt
index 7505ddf38..59f00dcbd 100644
--- a/2_GettingStarted.txt
+++ b/2_GettingStarted.txt
@@ -70,9 +70,9 @@ In addition, CaPTk offers the ability to extract and visualize commonly used mea
 
 References:
 
--# J.M.Soares, P.Marques, V.Alves, N.Sousa. "A hitchhiker's guide to diffusion tensor imaging", Front Neurosci. 7:31, 2013. DOI: 10.33
+-# J.M.Soares, P.Marques, V.Alves, N.Sousa. "A hitchhiker's guide to diffusion tensor imaging", Front Neurosci. 7:31, 2013, DOI: 10.33
 fnins.2013.00031
--# E.S.Paulson, K.M.Schmainda, "Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors", Radiology. 249(2):601-613, 2008. DOI: 10.1148/radiol.2492071659
+-# E.S.Paulson, K.M.Schmainda, "Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors", Radiology. 249(2):601-613, 2008, DOI: 10.1148/radiol.2492071659
 
 --------
 
@@ -182,13 +182,13 @@ Application-specific tissue types are automatically enabled when the correspondi
 
 References:
 
--# A.Gooya, K.M.Pohl, M.Billelo, G.Biros, C. Davatzikos, "Joint segmentation and deformable registration of brain scans guided by a tumog rowth model", Med Image Comput Comput Assist Interv. 14(Pt 2):532-40, 2011. DOI:10.1007/978-3-642-23629-7_65
--# A.Gooya, K.M.Pohl, M.Bilello, L.Cirillo, G.Biros, E.R.Melhem, C.Davatzikos, "GLISTR: glioma image segmentation and registration", IEET rans Med Imaging. 31(10):1941-54, 2012. DOI:10.1109/TMI.2012.2210558
--# D.Kwon, R.T.Shinohara, H.Akbari, C.Davatzikos, "Combining Generative Models for Multifocal Glioma Segmentation and Registration", MeI mage Comput Comput Assist Interv. 17(Pt 1):763-70, 2014. DOI:10.1007/978-3-319-10404-1_95
+-# A.Gooya, K.M.Pohl, M.Billelo, G.Biros, C. Davatzikos, "Joint segmentation and deformable registration of brain scans guided by a tumog rowth model", Med Image Comput Comput Assist Interv. 14(Pt 2):532-40, 2011, DOI:10.1007/978-3-642-23629-7_65
+-# A.Gooya, K.M.Pohl, M.Bilello, L.Cirillo, G.Biros, E.R.Melhem, C.Davatzikos, "GLISTR: glioma image segmentation and registration", IEET rans Med Imaging. 31(10):1941-54, 2012, DOI:10.1109/TMI.2012.2210558
+-# D.Kwon, R.T.Shinohara, H.Akbari, C.Davatzikos, "Combining Generative Models for Multifocal Glioma Segmentation and Registration", MeI mage Comput Comput Assist Interv. 17(Pt 1):763-70, 2014, DOI:10.1007/978-3-319-10404-1_95
 -# S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "Segmentation of gliomas in multimodam agnetic resonance imaging volumes based on a hybrid generative-discriminative framework", In Proc. Multimodal Brain Tumor Segmentation (BraTS) Challenge. 4:5-12, 2015.
--# S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "GLISTRboost: combining multimodal MRs egmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation", Brainlesion (2015). 9556:144-155, 2016. DOI:10.1007/978-3-319-30858-6_1
--# S.Bakas, H.Akbari, A.Sotiras, M.Bilello, M.Rozycki, J.Kirby, J.Freymann, K.Farahani, C.Davatzikos, "Advancing The Cancer Genome Atlag lioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data 4:170117, 2017. DOI:10.103s data.2017.117
--# D.Kwon, M.Niethammer, H.Akbari, M.Bilello, C.Davatzikos, K.M.Pohl, "PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration", IEEE Trans Med Imaging. 33(3):651-667, 2014. DOI:10.1109/TMI.2013.2293478
+-# S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "GLISTRboost: combining multimodal MRs egmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation", Brainlesion (2015). 9556:144-155, 2016, DOI:10.1007/978-3-319-30858-6_1
+-# S.Bakas, H.Akbari, A.Sotiras, M.Bilello, M.Rozycki, J.Kirby, J.Freymann, K.Farahani, C.Davatzikos, "Advancing The Cancer Genome Atlag lioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data 4:170117, 2017, DOI:10.103s data.2017.117
+-# D.Kwon, M.Niethammer, H.Akbari, M.Bilello, C.Davatzikos, K.M.Pohl, "PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration", IEEE Trans Med Imaging. 33(3):651-667, 2014, DOI:10.1109/TMI.2013.2293478
 
 --------
 
@@ -304,13 +304,13 @@ Preprocessing.exe -u
 
 References:
 
--# S.M.Smith, J.M.Brady, "SUSAN - a new approach to low level image processing", Int. J. Comput. Vis. 23(1):45-78, 1997. DOI:10.102A :1007963824710
--# N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans MeI maging. 29(6):1310-20, 2010. DOI: 10.1109/TMI.2010.2046908
+-# S.M.Smith, J.M.Brady, "SUSAN - a new approach to low level image processing", Int. J. Comput. Vis. 23(1):45-78, 1997, DOI:10.102A :1007963824710
+-# N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans MeI maging. 29(6):1310-20, 2010, DOI: 10.1109/TMI.2010.2046908
 -# S.P.Thakur, J.Doshi, S.Pati, S.M.Ha, C.Sako, S.Talbar, U.Kulkarni, C.Davatzikos, G.Erus, S.Bakas, "Brain Extraction on MRI Scans in Presence of Diffuse Glioma:
 Multi-institutional Performance Evaluation of Deep Learning Methods and Robust Modality-Agnostic Training", NeuroImage 2020, DOI:10.1016/j.neuroimage.2020.117081
 -# L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000, DOI:10.1109/42.836373
 -# P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI", Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127, DOI:10.1016/j.jalz.2016.06.205
--# T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010. DOI:10.1002/hbm.20906 
+-# T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010, DOI:10.1002/hbm.20906 
 
 --------
 
diff --git a/4_Science.txt b/4_Science.txt
index 00b7a5681..481391e74 100644
--- a/4_Science.txt
+++ b/4_Science.txt
@@ -8,11 +8,11 @@ This section presents examples of applications using CaPTk.
 
 Please make sure that whenever you use and/or refer to CaPTk in your manuscripts, you always cite the following paper:
 
-- C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018. DOI:10.1117/1.JMI.5.1.011018
+- C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018, DOI:10.1117/1.JMI.5.1.011018
   
 In addition, if the journal/conference you submit your paper does not restrict you from citing abstracts you might also cite the following:
 
-- S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017. DOI:10.1007/978-3-319-75238-9_12.
+- S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017, DOI:10.1007/978-3-319-75238-9_12.
 - S.Pati, S.Bakas, A.Sotiras, R.Kalarot, P.Sridharan, M.Bergman, S.Rathore, H.Akbari, P.Yushkevich, T.Shinohara, Y.Fan, D.Kontos, R.Verma, C.Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26–Dec.1, 2017, Chicago IL.
 - S.Pati, S.Rathore, R.Kalarot, P.Sridharan, M.Bergman, T.Shinohara, P.Yushkevich, Y.Fan, R.Verma, D.Kontos, C.Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html.
 
@@ -27,9 +27,9 @@ In addition, if the journal/conference you submit your paper does not restrict y
 
 References:
 
-- S.Bakas, H.Akbari, J.Pisapia, M.Martinez-Lage, M.Rozycki, S.Rathore, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the phi-index", Clin Cancer Res. 23(16):4724-4734, 2017. DOI:10.1158/1078-0432.CCR-16-1871
-- S.Bakas, H.Akbari, J.Pisapia, M.Rozycki, D.M.O'Rourke, C.Davatzikos. "Identification of Imaging Signatures of the Epidermal Growth Factor Receptor Variant III (EGFRvIII) in Glioblastoma", Neuro Oncol. 17(Suppl 5):v154, 2015. DOI:10.1093/neuonc/nov225.05
-- S.Bakas, Z.A.Binder, H.Akbari, M.Martinez-Lage, M.Rozycki, J.J.D.Morrissette, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "Highly-expressed wild-type EGFR and EGFRvIII mutant glioblastomas have similar MRI signature, consistent with deep peritumoral infiltration", Neuro Oncol. 18(Suppl 6):vi125-vi126, 2016. DOI:10.1093/neuonc/now212.523
+- S.Bakas, H.Akbari, J.Pisapia, M.Martinez-Lage, M.Rozycki, S.Rathore, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the phi-index", Clin Cancer Res. 23(16):4724-4734, 2017, DOI:10.1158/1078-0432.CCR-16-1871
+- S.Bakas, H.Akbari, J.Pisapia, M.Rozycki, D.M.O'Rourke, C.Davatzikos. "Identification of Imaging Signatures of the Epidermal Growth Factor Receptor Variant III (EGFRvIII) in Glioblastoma", Neuro Oncol. 17(Suppl 5):v154, 2015, DOI:10.1093/neuonc/nov225.05
+- S.Bakas, Z.A.Binder, H.Akbari, M.Martinez-Lage, M.Rozycki, J.J.D.Morrissette, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "Highly-expressed wild-type EGFR and EGFRvIII mutant glioblastomas have similar MRI signature, consistent with deep peritumoral infiltration", Neuro Oncol. 18(Suppl 6):vi125-vi126, 2016, DOI:10.1093/neuonc/now212.523
 
 --------
 --------
@@ -42,7 +42,7 @@ References:
 
 Reference:
 
-- L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016. DOI:10.1093/neuonc/nov127
+- L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016, DOI:10.1093/neuonc/nov127
 
 
 --------
@@ -56,8 +56,8 @@ Reference:
 
 References:
 
-- H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. "Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma", Neurosurgery. 78(4):572-80, 2016. DOI:10.1227/NEU.0000000000001202
-- H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014. DOI:10.1148/radiol.14132458
+- H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. "Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma", Neurosurgery. 78(4):572-80, 2016, DOI:10.1227/NEU.0000000000001202
+- H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014, DOI:10.1148/radiol.14132458
 - H.Akbari, L.Macyszyn, J.Pisapia, X.Da, M.Attiah, Y.Bi, S.Pal, R.Davuluri, L.Roccograndi, N.Dahmane, R.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos, "Survival Prediction in Glioblastoma Patients Using Multi-parametric MRI Biomarkers and Machine Learning Methods", American Society of Neuroradiology, O-525:2042-2044, 2015.
 
 --------
@@ -86,10 +86,10 @@ References:
 
 References:
 
-- A.Gastounioti, A.Oustimov, B.M.Keller, L.Pantalone, M.K.Hsieh, E.F.Conant, D.Kontos. "Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations", Med Phys. 43(11):5862-77, 2016. DOI: 10.1118/1.4963810
-- A.M.McCarthy, B.M.Keller, L.M.Pantalone, M.K.Hsieh, M.Synnestvedt, E.F.Conant, K.Armstrong, D.Kontos. "Racial differences in quantitative measures of area and volumetric breast density", J Natl Cancer Inst. 108(10), 2016. DOI:10.1093/jnci/djw104
-- E.F.Conant, B.M.Keller, L.Pantalone, A.Gastounioti, E.S.McDonald, D.Kontos. "Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures", Radiology. 283(3):673-80, 2017. DOI:10.1148/radiol.2016161286
-- A.D.Williams, A.So, M.Synnestvedt, C.M.Tewksbury, D.Kontos, M.K.Hsieh, L.Pantalone, E.F.Conant, M.Schnall, K.Dumon, N.Williams, J.Tchou. "Mammographic breast density decreases after bariatric surgery" Breast Cancer Res Treat. 165(3):565-572, 2017. DOI:10.1007/s10549-017-4361-y
+- A.Gastounioti, A.Oustimov, B.M.Keller, L.Pantalone, M.K.Hsieh, E.F.Conant, D.Kontos. "Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations", Med Phys. 43(11):5862-77, 2016, DOI: 10.1118/1.4963810
+- A.M.McCarthy, B.M.Keller, L.M.Pantalone, M.K.Hsieh, M.Synnestvedt, E.F.Conant, K.Armstrong, D.Kontos. "Racial differences in quantitative measures of area and volumetric breast density", J Natl Cancer Inst. 108(10), 2016, DOI:10.1093/jnci/djw104
+- E.F.Conant, B.M.Keller, L.Pantalone, A.Gastounioti, E.S.McDonald, D.Kontos. "Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures", Radiology. 283(3):673-80, 2017, DOI:10.1148/radiol.2016161286
+- A.D.Williams, A.So, M.Synnestvedt, C.M.Tewksbury, D.Kontos, M.K.Hsieh, L.Pantalone, E.F.Conant, M.Schnall, K.Dumon, N.Williams, J.Tchou. "Mammographic breast density decreases after bariatric surgery" Breast Cancer Res Treat. 165(3):565-572, 2017, DOI:10.1007/s10549-017-4361-y
 
 
 --------------------------------------------------------------------

From ef8b52588a72f2194f18ccd4ee74725a3efe2e02 Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:45:16 -0400
Subject: [PATCH 19/33] updateed citations

---
 4_Science.txt | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/4_Science.txt b/4_Science.txt
index 481391e74..766bca05e 100644
--- a/4_Science.txt
+++ b/4_Science.txt
@@ -6,15 +6,17 @@ This section presents examples of applications using CaPTk.
 
 \tableofcontents
 
-Please make sure that whenever you use and/or refer to CaPTk in your manuscripts, you always cite the following paper:
+Please make sure that whenever you use and/or refer to CaPTk in your research, you should always cite the following papers:
 
 - C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018, DOI:10.1117/1.JMI.5.1.011018
+- S.Pati, A.Singh, S.Rathore, A.Gastounioti, M.Bergman, P.Ngo, S.M.Ha, D.Bounias, J.Minock, G.Murphy, H.Li, A.Bhattarai, A.Wolf, P.Sridaran, R.Kalarot, H.Akbari, A.Sotiras, S.P.Thakur, R.Verma, R.T.Shinohara, P.Yushkevich, Y.Fan, D.Kontos, C.Davatzikos, S.Bakas, "The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview", Springer - BrainLes 2019 - LNCS, Vol.11993, 380-394, 2020, DOI:10.1007/978-3-030-46643-5_38
   
-In addition, if the journal/conference you submit your paper does not restrict you from citing abstracts you might also cite the following:
+In addition, if the journal/conference where you submit your paper does not restrict you from citing abstracts you might also cite the following:
 
-- S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017, DOI:10.1007/978-3-319-75238-9_12.
-- S.Pati, S.Bakas, A.Sotiras, R.Kalarot, P.Sridharan, M.Bergman, S.Rathore, H.Akbari, P.Yushkevich, T.Shinohara, Y.Fan, D.Kontos, R.Verma, C.Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26–Dec.1, 2017, Chicago IL.
-- S.Pati, S.Rathore, R.Kalarot, P.Sridharan, M.Bergman, T.Shinohara, P.Yushkevich, Y.Fan, R.Verma, D.Kontos, C.Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html.
+- RRID: SCR_017323 
+- S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017, DOI:10.1007/978-3-319-75238-9_12
+- S.Pati, S.Bakas, A.Sotiras, R.Kalarot, P.Sridharan, M.Bergman, S.Rathore, H.Akbari, P.Yushkevich, T.Shinohara, Y.Fan, D.Kontos, R.Verma, C.Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26-Dec.1, 2017, Chicago IL.
+- S.Pati, S.Rathore, R.Kalarot, P.Sridharan, M.Bergman, T.Shinohara, P.Yushkevich, Y.Fan, R.Verma, D.Kontos, C.Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html
 
 --------
 --------

From 191fe2c863d7d75ce2158f0d8e056201e505f80f Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:46:40 -0400
Subject: [PATCH 20/33] added github for sources

---
 6.1_Download.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/6.1_Download.txt b/6.1_Download.txt
index 2d178ffe2..0d22b287e 100644
--- a/6.1_Download.txt
+++ b/6.1_Download.txt
@@ -2,7 +2,7 @@
 
 \page Download Download Instructions
 
-Visit our Download Page hosted in NIH-supported NITRC (https://www.nitrc.org/frs/?group_id=1059), to download the CaPTk source code and binaries.
+Visit our Download Page hosted in NIH-supported NITRC (https://www.nitrc.org/frs/?group_id=1059), to download the CaPTk binaries and our GitHub page (https://github.com/CBICA/CaPTk) for the source code.
 
 CaPTk is currently distributed in the form of pre-compiled (executable) Windows, Linux (compiled on Ubuntu 16.04) and macOS (compiled on 10.13) installers with all dependencies integrated in the package.
 

From f7c000782dca177868613f1f17dbccd102321508 Mon Sep 17 00:00:00 2001
From: sarthakpati 
Date: Mon, 29 Jun 2020 09:47:19 -0400
Subject: [PATCH 21/33] html updates

---
 docs/BreastCancer_LIBRA.html              |  2 +-
 docs/BreastCancer_breastSegmentation.html |  2 +-
 docs/BreastCancer_texture.html            |  2 +-
 docs/Download.html                        |  8 +++---
 docs/Getting_Started.html                 | 22 +++++++--------
 docs/Glioblastoma_Atlas.html              |  2 +-
 docs/Glioblastoma_Confetti.html           | 10 +++----
 docs/Glioblastoma_Directionality.html     |  2 +-
 docs/Glioblastoma_Molecular.html          |  2 +-
 docs/Glioblastoma_PHI.html                |  6 ++--
 docs/Glioblastoma_Recurrence.html         |  4 +--
 docs/Glioblastoma_Survival.html           |  2 +-
 docs/Glioblastoma_WhiteStripe.html        |  2 +-
 docs/PCA_Extraction.html                  |  2 +-
 docs/Science.html                         | 34 ++++++++++++-----------
 docs/gs_preprocessing.html                |  8 +++---
 docs/gs_seedpoints.html                   | 12 ++++----
 docs/gs_supportedImages.html              |  4 +--
 docs/index.html                           | 18 ++++++++++--
 docs/index.qhp                            |  7 +++--
 docs/libraPapers.html                     |  8 +++---
 docs/navtreedata.js                       |  7 +++--
 docs/navtreeindex0.js                     |  5 ++--
 docs/phiEstimator.html                    |  6 ++--
 docs/preprocessing_bias.html              |  4 +--
 docs/preprocessing_brats.html             |  6 ++--
 docs/preprocessing_histoMatch.html        |  2 +-
 docs/preprocessing_reg.html               |  2 +-
 docs/preprocessing_susan.html             |  2 +-
 docs/preprocessing_zScoreNorm.html        |  2 +-
 docs/recurrencePredictor.html             |  4 +--
 docs/seg_DL.html                          |  2 +-
 docs/seg_GeoTrain.html                    |  2 +-
 docs/seg_SNAP.html                        |  2 +-
 docs/survivalPredictor.html               |  2 +-
 35 files changed, 113 insertions(+), 94 deletions(-)

diff --git a/docs/BreastCancer_LIBRA.html b/docs/BreastCancer_LIBRA.html
index 93c8d50f7..1f0bd1194 100644
--- a/docs/BreastCancer_LIBRA.html
+++ b/docs/BreastCancer_LIBRA.html
@@ -107,7 +107,7 @@
 
      
 
      References:
      
 
        
-
      1. B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012. DOI:10.1118/1.4736530
        2. 
+
      2. B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012, DOI:10.1118/1.4736530
        3. 
 
      
 
      
  
    
diff --git a/docs/BreastCancer_breastSegmentation.html b/docs/BreastCancer_breastSegmentation.html
index bea629375..a60c9342a 100644
--- a/docs/BreastCancer_breastSegmentation.html
+++ b/docs/BreastCancer_breastSegmentation.html
@@ -75,7 +75,7 @@
 
    
 
    References:
    
 
      
-
    1. B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012. DOI:10.1118/1.4736530
      2. 
+
    2. B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012, DOI:10.1118/1.4736530
      3.

    7. diff --git a/docs/BreastCancer_texture.html b/docs/BreastCancer_texture.html index f2190308a..1beb8bafe 100644 --- a/docs/BreastCancer_texture.html +++ b/docs/BreastCancer_texture.html @@ -79,7 +79,7 @@

    References:

      -
    1. B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012. DOI:10.1118/1.4736530
      2. +
    2. B.M.Keller, D.L.Nathan, Y.Wang, Y.Zheng, J.C.Gee, E.F.Conant, D.Kontos, "Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation", Med Phys. 39(8):4903-4917, 2012, DOI:10.1118/1.4736530
    diff --git a/docs/Download.html b/docs/Download.html index 278035e13..19b5ae318 100644 --- a/docs/Download.html +++ b/docs/Download.html @@ -61,20 +61,20 @@
    Download Instructions
    -

    Visit our Download Page hosted in NIH-supported NITRC (https://www.nitrc.org/frs/?group_id=1059), to download the CaPTk source code and binaries.

    +

    Visit our Download Page hosted in NIH-supported NITRC (https://www.nitrc.org/frs/?group_id=1059), to download the CaPTk binaries and our GitHub page (https://github.com/CBICA/CaPTk) for the source code.

    CaPTk is currently distributed in the form of pre-compiled (executable) Windows, Linux (compiled on Ubuntu 16.04) and macOS (compiled on 10.13) installers with all dependencies integrated in the package.

    Check the Installation guide for details on installation.

    Please make sure that whenever you use and/or refer to CaPTk in your research, you should always cite the following papers:

      -
    • C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018. DOI:10.1117/1.JMI.5.1.011018
      • -
    • S.Pati, A.Singh, S.Rathore, A.Gastounioti, M.Bergman, P.Ngo, S.M.Ha, D.Bounias, J.Minock, G.Murphy, H.Li, A.Bhattarai, A.Wolf, P.Sridaran, R.Kalarot, H.Akbari, A.Sotiras, S.P.Thakur, R.Verma, R.T.Shinohara, P.Yushkevich, Y.Fan, D.Kontos, C.Davatzikos, S.Bakas, "The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview", Springer - BrainLes 2019 - LNCS, Vol.11993, 380-394, 2020. DOI: 10.1007/978-3-030-46643-5_38
      • +
    • C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018, DOI:10.1117/1.JMI.5.1.011018
      • +
    • S.Pati, A.Singh, S.Rathore, A.Gastounioti, M.Bergman, P.Ngo, S.M.Ha, D.Bounias, J.Minock, G.Murphy, H.Li, A.Bhattarai, A.Wolf, P.Sridaran, R.Kalarot, H.Akbari, A.Sotiras, S.P.Thakur, R.Verma, R.T.Shinohara, P.Yushkevich, Y.Fan, D.Kontos, C.Davatzikos, S.Bakas, "The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview", Springer - BrainLes 2019 - LNCS, Vol.11993, 380-394, 2020, DOI:10.1007/978-3-030-46643-5_38

    In addition, if the journal/conference where you submit your paper does not restrict you from citing abstracts you might also cite the following:

    • RRID: SCR_017323
      • -
    • S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017. DOI:10.1007/978-3-319-75238-9_12
      • +
    • S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017, DOI:10.1007/978-3-319-75238-9_12
    • S.Pati, S.Bakas, A.Sotiras, R.Kalarot, P.Sridharan, M.Bergman, S.Rathore, H.Akbari, P.Yushkevich, T.Shinohara, Y.Fan, D.Kontos, R.Verma, C.Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26-Dec.1, 2017, Chicago IL.
    • S.Pati, S.Rathore, R.Kalarot, P.Sridharan, M.Bergman, T.Shinohara, P.Yushkevich, Y.Fan, R.Verma, D.Kontos, C.Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html
    diff --git a/docs/Getting_Started.html b/docs/Getting_Started.html index 1372332cf..ed9a1b92c 100644 --- a/docs/Getting_Started.html +++ b/docs/Getting_Started.html @@ -158,8 +158,8 @@

    References:

      -
    1. J.M.Soares, P.Marques, V.Alves, N.Sousa. "A hitchhiker's guide to diffusion tensor imaging", Front Neurosci. 7:31, 2013. DOI: 10.33 fnins.2013.00031
      2. -
    2. E.S.Paulson, K.M.Schmainda, "Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors", Radiology. 249(2):601-613, 2008. DOI: 10.1148/radiol.2492071659
      3. +
    3. J.M.Soares, P.Marques, V.Alves, N.Sousa. "A hitchhiker's guide to diffusion tensor imaging", Front Neurosci. 7:31, 2013, DOI: 10.33 fnins.2013.00031
      4. +
    4. E.S.Paulson, K.M.Schmainda, "Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors", Radiology. 249(2):601-613, 2008, DOI: 10.1148/radiol.2492071659

    @@ -279,13 +279,13 @@

    References:

      -
    1. A.Gooya, K.M.Pohl, M.Billelo, G.Biros, C. Davatzikos, "Joint segmentation and deformable registration of brain scans guided by a tumog rowth model", Med Image Comput Comput Assist Interv. 14(Pt 2):532-40, 2011. DOI:10.1007/978-3-642-23629-7_65
      2. -
    2. A.Gooya, K.M.Pohl, M.Bilello, L.Cirillo, G.Biros, E.R.Melhem, C.Davatzikos, "GLISTR: glioma image segmentation and registration", IEET rans Med Imaging. 31(10):1941-54, 2012. DOI:10.1109/TMI.2012.2210558
      3. -
    3. D.Kwon, R.T.Shinohara, H.Akbari, C.Davatzikos, "Combining Generative Models for Multifocal Glioma Segmentation and Registration", MeI mage Comput Comput Assist Interv. 17(Pt 1):763-70, 2014. DOI:10.1007/978-3-319-10404-1_95
      4. +
    4. A.Gooya, K.M.Pohl, M.Billelo, G.Biros, C. Davatzikos, "Joint segmentation and deformable registration of brain scans guided by a tumog rowth model", Med Image Comput Comput Assist Interv. 14(Pt 2):532-40, 2011, DOI:10.1007/978-3-642-23629-7_65
      5. +
    5. A.Gooya, K.M.Pohl, M.Bilello, L.Cirillo, G.Biros, E.R.Melhem, C.Davatzikos, "GLISTR: glioma image segmentation and registration", IEET rans Med Imaging. 31(10):1941-54, 2012, DOI:10.1109/TMI.2012.2210558
      6. +
    6. D.Kwon, R.T.Shinohara, H.Akbari, C.Davatzikos, "Combining Generative Models for Multifocal Glioma Segmentation and Registration", MeI mage Comput Comput Assist Interv. 17(Pt 1):763-70, 2014, DOI:10.1007/978-3-319-10404-1_95
    7. S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "Segmentation of gliomas in multimodam agnetic resonance imaging volumes based on a hybrid generative-discriminative framework", In Proc. Multimodal Brain Tumor Segmentation (BraTS) Challenge. 4:5-12, 2015.
      8. -
    8. S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "GLISTRboost: combining multimodal MRs egmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation", Brainlesion (2015). 9556:144-155, 2016. DOI:10.1007/978-3-319-30858-6_1
      9. -
    9. S.Bakas, H.Akbari, A.Sotiras, M.Bilello, M.Rozycki, J.Kirby, J.Freymann, K.Farahani, C.Davatzikos, "Advancing The Cancer Genome Atlag lioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data 4:170117, 2017. DOI:10.103s data.2017.117
      10. -
    10. D.Kwon, M.Niethammer, H.Akbari, M.Bilello, C.Davatzikos, K.M.Pohl, "PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration", IEEE Trans Med Imaging. 33(3):651-667, 2014. DOI:10.1109/TMI.2013.2293478
      11. +
    11. S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "GLISTRboost: combining multimodal MRs egmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation", Brainlesion (2015). 9556:144-155, 2016, DOI:10.1007/978-3-319-30858-6_1
      12. +
    12. S.Bakas, H.Akbari, A.Sotiras, M.Bilello, M.Rozycki, J.Kirby, J.Freymann, K.Farahani, C.Davatzikos, "Advancing The Cancer Genome Atlag lioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data 4:170117, 2017, DOI:10.103s data.2017.117
      13. +
    13. D.Kwon, M.Niethammer, H.Akbari, M.Bilello, C.Davatzikos, K.M.Pohl, "PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration", IEEE Trans Med Imaging. 33(3):651-667, 2014, DOI:10.1109/TMI.2013.2293478

    @@ -415,13 +415,13 @@

    References:

      -
    1. S.M.Smith, J.M.Brady, "SUSAN - a new approach to low level image processing", Int. J. Comput. Vis. 23(1):45-78, 1997. DOI:10.102A :1007963824710
      2. -
    2. N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans MeI maging. 29(6):1310-20, 2010. DOI: 10.1109/TMI.2010.2046908
      3. +
    3. S.M.Smith, J.M.Brady, "SUSAN - a new approach to low level image processing", Int. J. Comput. Vis. 23(1):45-78, 1997, DOI:10.102A :1007963824710
      4. +
    4. N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans MeI maging. 29(6):1310-20, 2010, DOI: 10.1109/TMI.2010.2046908
    5. S.P.Thakur, J.Doshi, S.Pati, S.M.Ha, C.Sako, S.Talbar, U.Kulkarni, C.Davatzikos, G.Erus, S.Bakas, "Brain Extraction on MRI Scans in Presence of Diffuse Glioma: Multi-institutional Performance Evaluation of Deep Learning Methods and Robust Modality-Agnostic Training", NeuroImage 2020, DOI:10.1016/j.neuroimage.2020.117081
    6. L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000, DOI:10.1109/42.836373
    7. P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI", Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127, DOI:10.1016/j.jalz.2016.06.205
      8. -
    8. T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010. DOI:10.1002/hbm.20906
      9. +
    9. T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010, DOI:10.1002/hbm.20906

    diff --git a/docs/Glioblastoma_Atlas.html b/docs/Glioblastoma_Atlas.html index fe839f154..05751fabe 100644 --- a/docs/Glioblastoma_Atlas.html +++ b/docs/Glioblastoma_Atlas.html @@ -84,7 +84,7 @@

    References:

      -
    1. M. Bilello, H. Akbari, X. Da, J.M.Pisapia, S.Mohan, R.L.Wolf, D.M.O'Rourke, M.Martinez-Lage, C.Davatzikos. "Population-based MRI atlases of spatial distribution are specific to patient and tumor characteristics in glioblastoma", Neuroimage Clin. 12:34-40, 2016. DOI:10.1016/j.nicl.2016.03.007
      2. +
    2. M. Bilello, H. Akbari, X. Da, J.M.Pisapia, S.Mohan, R.L.Wolf, D.M.O'Rourke, M.Martinez-Lage, C.Davatzikos. "Population-based MRI atlases of spatial distribution are specific to patient and tumor characteristics in glioblastoma", Neuroimage Clin. 12:34-40, 2016, DOI:10.1016/j.nicl.2016.03.007

    diff --git a/docs/Glioblastoma_Confetti.html b/docs/Glioblastoma_Confetti.html index 722b384ff..6ce357c45 100644 --- a/docs/Glioblastoma_Confetti.html +++ b/docs/Glioblastoma_Confetti.html @@ -98,12 +98,12 @@

    References:

      -
    1. B.Tunc, M.Ingalhalikar, W.A.Parker, J.Lecoeur, N.Singh, R.L.Wolf, L.Macyszyn, S.Brem, R.Verma, "Individualized Map of White Matter Pathways: Connectivity-based Paradigm for Neurosurgical Planning", Neurosurgery. 79(4):568-77, 2016. DOI:10.1227/NEU.0000000000001183.
      2. -
    2. B.Tunc, W.A.Parker, M.Ingalhalikar, R.Verma, "Automated tract extraction via atlas based Adaptive Clustering", NeuroImage. 102(2):596-607, 2014. DOI:10.1016/j.neuroimage.2014.08.021
      3. +
    3. B.Tunc, M.Ingalhalikar, W.A.Parker, J.Lecoeur, N.Singh, R.L.Wolf, L.Macyszyn, S.Brem, R.Verma, "Individualized Map of White Matter Pathways: Connectivity-based Paradigm for Neurosurgical Planning", Neurosurgery. 79(4):568-77, 2016, DOI:10.1227/NEU.0000000000001183.
      4. +
    4. B.Tunc, W.A.Parker, M.Ingalhalikar, R.Verma, "Automated tract extraction via atlas based Adaptive Clustering", NeuroImage. 102(2):596-607, 2014, DOI:10.1016/j.neuroimage.2014.08.021
    5. B.Tunc, A.R.Smith, D.Wasserman, X.Pennec, W.M.Wells, R.Verma, K.M.Pohl, "Multinomial Probabilistic Fiber Representation for Connectivity Driven Clustering", Inf Process Med Imaging. 23:730-41, 2013.
      6. -
    6. B.Fischl, M.I.Sereno, A.M.Dale, "Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system", NeuroImage. 9:195-207, 1999. DOI:10.1006/nimg.1998.0396
      7. -
    7. R.S.Desikan, F.Segonne, B.Fischl, B.Quinn, B.Dickerson, D.Blacker, R.Buckner, A.Dale, R.Maguire, B.Hyman, M.Albert, R.Killiany, "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest", NeuroImage. 31(3):968-80, 2006. DOI:10.1016/j.neuroimage.2006.01.021
      8. -
    8. M.Jenkinson, C.F.Beckmann, T.E.J.Behrens, M.W.Woolrich, S.M.Smith, "FSL", Neuroimage. 62(2):782-790, 2012. DOI:10.1016/j.neuroimage.2011.09.015
      9. +
    9. B.Fischl, M.I.Sereno, A.M.Dale, "Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system", NeuroImage. 9:195-207, 1999, DOI:10.1006/nimg.1998.0396
      10. +
    10. R.S.Desikan, F.Segonne, B.Fischl, B.Quinn, B.Dickerson, D.Blacker, R.Buckner, A.Dale, R.Maguire, B.Hyman, M.Albert, R.Killiany, "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest", NeuroImage. 31(3):968-80, 2006, DOI:10.1016/j.neuroimage.2006.01.021
      11. +
    11. M.Jenkinson, C.F.Beckmann, T.E.J.Behrens, M.W.Woolrich, S.M.Smith, "FSL", Neuroimage. 62(2):782-790, 2012, DOI:10.1016/j.neuroimage.2011.09.015
    diff --git a/docs/Glioblastoma_Directionality.html b/docs/Glioblastoma_Directionality.html index a2424e652..255d08b05 100644 --- a/docs/Glioblastoma_Directionality.html +++ b/docs/Glioblastoma_Directionality.html @@ -94,7 +94,7 @@

    References:

      -
    1. M.E.Schweitzer, M.A.Stavarache, N.Petersen, S.Bakas, A.J.Tsiouris, C.Davatzikos, M.G.Kaplitt, M.M.Souweidane, "Modulation of Convection Enhanced Delivery (CED) distribution using Focused Ultrasound (FUS)", Neuro Oncol. 19(Suppl 6):vi272, 2017. DOI:10.1093/neuonc/nox168.1118
      2. +
    2. M.E.Schweitzer, M.A.Stavarache, N.Petersen, S.Bakas, A.J.Tsiouris, C.Davatzikos, M.G.Kaplitt, M.M.Souweidane, "Modulation of Convection Enhanced Delivery (CED) distribution using Focused Ultrasound (FUS)", Neuro Oncol. 19(Suppl 6):vi272, 2017, DOI:10.1093/neuonc/nox168.1118
    diff --git a/docs/Glioblastoma_Molecular.html b/docs/Glioblastoma_Molecular.html index f3d806f17..ec9c6da7c 100644 --- a/docs/Glioblastoma_Molecular.html +++ b/docs/Glioblastoma_Molecular.html @@ -136,7 +136,7 @@

    References:

      -
    1. L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016. DOI:10.1093/neuonc/nov127.
      2. +
    2. L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016, DOI:10.1093/neuonc/nov127.
    diff --git a/docs/Glioblastoma_PHI.html b/docs/Glioblastoma_PHI.html index b7a162abf..6cf68f6be 100644 --- a/docs/Glioblastoma_PHI.html +++ b/docs/Glioblastoma_PHI.html @@ -94,9 +94,9 @@

    References:

      -
    1. S.Bakas, H.Akbari, J.Pisapia, M.Rozycki, D.M.O'Rourke, C.Davatzikos. "Identification of Imaging Signatures of the Epidermal Growth Factor Receptor Variant III (EGFRvIII) in Glioblastoma", Neuro Oncol. 17(Suppl 5):v154, 2015. DOI:10.1093/neuonc/nov225.05
      2. -
    2. S.Bakas, Z.A.Binder, H.Akbari, M.Martinez-Lage, M.Rozycki, J.J.D.Morrissette, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "Highly-expressed wild-type EGFR and EGFRvIII mutant glioblastomas have similar MRI signature, consistent with deep peritumoral infiltration", Neuro Oncol. 18(Suppl 6):vi125-vi126, 2016. DOI:10.1093/neuonc/now212.523
      3. -
    3. S.Bakas, H.Akbari, J.Pisapia, M.Martinez-Lage, M.Rozycki, S.Rathore, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the phi-index", Clin Cancer Res. 23(16):4724-4734, 2017. DOI:10.1158/1078-0432.CCR-16-1871
      4. +
    4. S.Bakas, H.Akbari, J.Pisapia, M.Rozycki, D.M.O'Rourke, C.Davatzikos. "Identification of Imaging Signatures of the Epidermal Growth Factor Receptor Variant III (EGFRvIII) in Glioblastoma", Neuro Oncol. 17(Suppl 5):v154, 2015, DOI:10.1093/neuonc/nov225.05
      5. +
    5. S.Bakas, Z.A.Binder, H.Akbari, M.Martinez-Lage, M.Rozycki, J.J.D.Morrissette, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "Highly-expressed wild-type EGFR and EGFRvIII mutant glioblastomas have similar MRI signature, consistent with deep peritumoral infiltration", Neuro Oncol. 18(Suppl 6):vi125-vi126, 2016, DOI:10.1093/neuonc/now212.523
      6. +
    6. S.Bakas, H.Akbari, J.Pisapia, M.Martinez-Lage, M.Rozycki, S.Rathore, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the phi-index", Clin Cancer Res. 23(16):4724-4734, 2017, DOI:10.1158/1078-0432.CCR-16-1871
    diff --git a/docs/Glioblastoma_Recurrence.html b/docs/Glioblastoma_Recurrence.html index bff93079f..48e5c0bee 100644 --- a/docs/Glioblastoma_Recurrence.html +++ b/docs/Glioblastoma_Recurrence.html @@ -149,9 +149,9 @@

    References:

      -
    1. H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014. DOI:10.1148/radiol.14132458
      2. +
    2. H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014, DOI:10.1148/radiol.14132458
    3. H.Akbari, L.Macyszyn, J.Pisapia, X.Da, M.Attiah, Y.Bi, S.Pal, R.Davuluri, L.Roccograndi, N.Dahmane, R.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos, "Survival Prediction in Glioblastoma Patients Using Multi-parametric MRI Biomarkers and Machine Learning Methods", American Society of Neuroradiology, O-525:2042-2044, 2015. (http://www.asnr.org/sites/default/files/proceedings/2015_Proceedings.pdf)
      4. -
    4. H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. "Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma", Neurosurgery. 78(4):572-80, 2016. DOI:10.1227/NEU.0000000000001202
      5. +
    5. H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. "Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma", Neurosurgery. 78(4):572-80, 2016, DOI:10.1227/NEU.0000000000001202
    diff --git a/docs/Glioblastoma_Survival.html b/docs/Glioblastoma_Survival.html index 7098edb47..a9f6a1cd4 100644 --- a/docs/Glioblastoma_Survival.html +++ b/docs/Glioblastoma_Survival.html @@ -136,7 +136,7 @@

    References:

      -
    1. L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016. DOI:10.1093/neuonc/nov127
      2. +
    2. L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016, DOI:10.1093/neuonc/nov127
    diff --git a/docs/Glioblastoma_WhiteStripe.html b/docs/Glioblastoma_WhiteStripe.html index 380a63483..0fd6b3439 100644 --- a/docs/Glioblastoma_WhiteStripe.html +++ b/docs/Glioblastoma_WhiteStripe.html @@ -80,7 +80,7 @@

    References:

      -
    1. R.T.Shinohara, E.M.Sweeney, J.Goldsmith, N.Shiee, F.J.Mateen, P.A.Calabresi, S.Jarso, D.L.Pham, D.S.Reich, C.M.Crainiceanu, Australian Imaging Biomarkers Lifestyle Flagship Study of Ageing, Alzheimer's Disease Neuroimaging Initiative. "Statistical normalization techniques for magnetic resonance imaging", Neuroimage Clin. 6:9-19, 2014. DOI:10.1016/j.nicl.2014.08.008
      2. +
    2. R.T.Shinohara, E.M.Sweeney, J.Goldsmith, N.Shiee, F.J.Mateen, P.A.Calabresi, S.Jarso, D.L.Pham, D.S.Reich, C.M.Crainiceanu, Australian Imaging Biomarkers Lifestyle Flagship Study of Ageing, Alzheimer's Disease Neuroimaging Initiative. "Statistical normalization techniques for magnetic resonance imaging", Neuroimage Clin. 6:9-19, 2014, DOI:10.1016/j.nicl.2014.08.008
    diff --git a/docs/PCA_Extraction.html b/docs/PCA_Extraction.html index 2074ffa86..29c0b2581 100644 --- a/docs/PCA_Extraction.html +++ b/docs/PCA_Extraction.html @@ -80,7 +80,7 @@

    References:

      -
    1. H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014. DOI:10.1148/radiol.14132458
      2. +
    2. H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014, DOI:10.1148/radiol.14132458
    diff --git a/docs/Science.html b/docs/Science.html index f563b65f5..10fe6a2c7 100644 --- a/docs/Science.html +++ b/docs/Science.html @@ -70,15 +70,17 @@

    This section presents examples of applications using CaPTk.

    -

    Please make sure that whenever you use and/or refer to CaPTk in your manuscripts, you always cite the following paper:

    +

    Please make sure that whenever you use and/or refer to CaPTk in your research, you should always cite the following papers:

      -
    • C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018. DOI:10.1117/1.JMI.5.1.011018
      • +
    • C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018, DOI:10.1117/1.JMI.5.1.011018
      • +
    • S.Pati, A.Singh, S.Rathore, A.Gastounioti, M.Bergman, P.Ngo, S.M.Ha, D.Bounias, J.Minock, G.Murphy, H.Li, A.Bhattarai, A.Wolf, P.Sridaran, R.Kalarot, H.Akbari, A.Sotiras, S.P.Thakur, R.Verma, R.T.Shinohara, P.Yushkevich, Y.Fan, D.Kontos, C.Davatzikos, S.Bakas, "The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview", Springer - BrainLes 2019 - LNCS, Vol.11993, 380-394, 2020, DOI:10.1007/978-3-030-46643-5_38
    -

    In addition, if the journal/conference you submit your paper does not restrict you from citing abstracts you might also cite the following:

    +

    In addition, if the journal/conference where you submit your paper does not restrict you from citing abstracts you might also cite the following:

      -
    • S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017. DOI:10.1007/978-3-319-75238-9_12.
      • -
    • S.Pati, S.Bakas, A.Sotiras, R.Kalarot, P.Sridharan, M.Bergman, S.Rathore, H.Akbari, P.Yushkevich, T.Shinohara, Y.Fan, D.Kontos, R.Verma, C.Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26–Dec.1, 2017, Chicago IL.
      • -
    • S.Pati, S.Rathore, R.Kalarot, P.Sridharan, M.Bergman, T.Shinohara, P.Yushkevich, Y.Fan, R.Verma, D.Kontos, C.Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html.
      • +
    • RRID: SCR_017323
      • +
    • S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017, DOI:10.1007/978-3-319-75238-9_12
      • +
    • S.Pati, S.Bakas, A.Sotiras, R.Kalarot, P.Sridharan, M.Bergman, S.Rathore, H.Akbari, P.Yushkevich, T.Shinohara, Y.Fan, D.Kontos, R.Verma, C.Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26-Dec.1, 2017, Chicago IL.
      • +
    • S.Pati, S.Rathore, R.Kalarot, P.Sridharan, M.Bergman, T.Shinohara, P.Yushkevich, Y.Fan, R.Verma, D.Kontos, C.Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html
    @@ -91,9 +93,9 @@

    References:

      -
    • S.Bakas, H.Akbari, J.Pisapia, M.Martinez-Lage, M.Rozycki, S.Rathore, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the phi-index", Clin Cancer Res. 23(16):4724-4734, 2017. DOI:10.1158/1078-0432.CCR-16-1871
      • -
    • S.Bakas, H.Akbari, J.Pisapia, M.Rozycki, D.M.O'Rourke, C.Davatzikos. "Identification of Imaging Signatures of the Epidermal Growth Factor Receptor Variant III (EGFRvIII) in Glioblastoma", Neuro Oncol. 17(Suppl 5):v154, 2015. DOI:10.1093/neuonc/nov225.05
      • -
    • S.Bakas, Z.A.Binder, H.Akbari, M.Martinez-Lage, M.Rozycki, J.J.D.Morrissette, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "Highly-expressed wild-type EGFR and EGFRvIII mutant glioblastomas have similar MRI signature, consistent with deep peritumoral infiltration", Neuro Oncol. 18(Suppl 6):vi125-vi126, 2016. DOI:10.1093/neuonc/now212.523
      • +
    • S.Bakas, H.Akbari, J.Pisapia, M.Martinez-Lage, M.Rozycki, S.Rathore, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the phi-index", Clin Cancer Res. 23(16):4724-4734, 2017, DOI:10.1158/1078-0432.CCR-16-1871
      • +
    • S.Bakas, H.Akbari, J.Pisapia, M.Rozycki, D.M.O'Rourke, C.Davatzikos. "Identification of Imaging Signatures of the Epidermal Growth Factor Receptor Variant III (EGFRvIII) in Glioblastoma", Neuro Oncol. 17(Suppl 5):v154, 2015, DOI:10.1093/neuonc/nov225.05
      • +
    • S.Bakas, Z.A.Binder, H.Akbari, M.Martinez-Lage, M.Rozycki, J.J.D.Morrissette, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "Highly-expressed wild-type EGFR and EGFRvIII mutant glioblastomas have similar MRI signature, consistent with deep peritumoral infiltration", Neuro Oncol. 18(Suppl 6):vi125-vi126, 2016, DOI:10.1093/neuonc/now212.523
    @@ -106,7 +108,7 @@

    Reference:

      -
    • L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016. DOI:10.1093/neuonc/nov127
      • +
    • L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016, DOI:10.1093/neuonc/nov127
    @@ -119,8 +121,8 @@

    References:

      -
    • H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. "Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma", Neurosurgery. 78(4):572-80, 2016. DOI:10.1227/NEU.0000000000001202
      • -
    • H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014. DOI:10.1148/radiol.14132458
      • +
    • H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. "Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma", Neurosurgery. 78(4):572-80, 2016, DOI:10.1227/NEU.0000000000001202
      • +
    • H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014, DOI:10.1148/radiol.14132458
    • H.Akbari, L.Macyszyn, J.Pisapia, X.Da, M.Attiah, Y.Bi, S.Pal, R.Davuluri, L.Roccograndi, N.Dahmane, R.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos, "Survival Prediction in Glioblastoma Patients Using Multi-parametric MRI Biomarkers and Machine Learning Methods", American Society of Neuroradiology, O-525:2042-2044, 2015.
    @@ -149,10 +151,10 @@

    References:

      -
    • A.Gastounioti, A.Oustimov, B.M.Keller, L.Pantalone, M.K.Hsieh, E.F.Conant, D.Kontos. "Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations", Med Phys. 43(11):5862-77, 2016. DOI: 10.1118/1.4963810
      • -
    • A.M.McCarthy, B.M.Keller, L.M.Pantalone, M.K.Hsieh, M.Synnestvedt, E.F.Conant, K.Armstrong, D.Kontos. "Racial differences in quantitative measures of area and volumetric breast density", J Natl Cancer Inst. 108(10), 2016. DOI:10.1093/jnci/djw104
      • -
    • E.F.Conant, B.M.Keller, L.Pantalone, A.Gastounioti, E.S.McDonald, D.Kontos. "Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures", Radiology. 283(3):673-80, 2017. DOI:10.1148/radiol.2016161286
      • -
    • A.D.Williams, A.So, M.Synnestvedt, C.M.Tewksbury, D.Kontos, M.K.Hsieh, L.Pantalone, E.F.Conant, M.Schnall, K.Dumon, N.Williams, J.Tchou. "Mammographic breast density decreases after bariatric surgery" Breast Cancer Res Treat. 165(3):565-572, 2017. DOI:10.1007/s10549-017-4361-y
      • +
    • A.Gastounioti, A.Oustimov, B.M.Keller, L.Pantalone, M.K.Hsieh, E.F.Conant, D.Kontos. "Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations", Med Phys. 43(11):5862-77, 2016, DOI: 10.1118/1.4963810
      • +
    • A.M.McCarthy, B.M.Keller, L.M.Pantalone, M.K.Hsieh, M.Synnestvedt, E.F.Conant, K.Armstrong, D.Kontos. "Racial differences in quantitative measures of area and volumetric breast density", J Natl Cancer Inst. 108(10), 2016, DOI:10.1093/jnci/djw104
      • +
    • E.F.Conant, B.M.Keller, L.Pantalone, A.Gastounioti, E.S.McDonald, D.Kontos. "Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures", Radiology. 283(3):673-80, 2017, DOI:10.1148/radiol.2016161286
      • +
    • A.D.Williams, A.So, M.Synnestvedt, C.M.Tewksbury, D.Kontos, M.K.Hsieh, L.Pantalone, E.F.Conant, M.Schnall, K.Dumon, N.Williams, J.Tchou. "Mammographic breast density decreases after bariatric surgery" Breast Cancer Res Treat. 165(3):565-572, 2017, DOI:10.1007/s10549-017-4361-y
    diff --git a/docs/gs_preprocessing.html b/docs/gs_preprocessing.html index 944e20c23..6349d3453 100644 --- a/docs/gs_preprocessing.html +++ b/docs/gs_preprocessing.html @@ -95,18 +95,18 @@

    References:

    1. -S.M.Smith, J.M.Brady, "SUSAN - a new approach to low level image processing", Int. J. Comput. Vis. 23(1):45-78, 1997. DOI:10.1023/A:1007963824710
      2. +S.M.Smith, J.M.Brady, "SUSAN - a new approach to low level image processing", Int. J. Comput. Vis. 23(1):45-78, 1997, DOI:10.1023/A:1007963824710
    2. -N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans Med Imaging. 29(6):1310-20, 2010. doi: 10.1109/TMI.2010.2046908
      3. +N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans Med Imaging. 29(6):1310-20, 2010, DOI: 10.1109/TMI.2010.2046908
    3. S.Bauer, L.P.Nolte, M.Reyes, "Skull-stripping for Tumor-bearing Brain Images", arXiv. abs/1204.0357, 2012.
    4. -L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000. DOI:10.1109/42.836373
      5. +L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000, DOI:10.1109/42.836373
    5. P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI, Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127
    6. -T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010. DOI:10.1002/hbm.20906
      7. +T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010, DOI:10.1002/hbm.20906

    diff --git a/docs/gs_seedpoints.html b/docs/gs_seedpoints.html index 648514990..2d047c343 100644 --- a/docs/gs_seedpoints.html +++ b/docs/gs_seedpoints.html @@ -125,19 +125,19 @@

    Tissue Points

    References:

    1. -A.Gooya, K.M.Pohl, M.Billelo, G.Biros, C. Davatzikos, "Joint segmentation and deformable registration of brain scans guided by a tumor growth model", Med Image Comput Comput Assist Interv. 14(Pt 2):532-40, 2011. DOI:10.1007/978-3-642-23629-7_65
      2. +A.Gooya, K.M.Pohl, M.Billelo, G.Biros, C. Davatzikos, "Joint segmentation and deformable registration of brain scans guided by a tumor growth model", Med Image Comput Comput Assist Interv. 14(Pt 2):532-40, 2011, DOI:10.1007/978-3-642-23629-7_65
    2. -A.Gooya, K.M.Pohl, M.Bilello, L.Cirillo, G.Biros, E.R.Melhem, C.Davatzikos, "GLISTR: glioma image segmentation and registration", IEEE Trans Med Imaging. 31(10):1941-54, 2012. DOI:10.1109/TMI.2012.2210558
      3. +A.Gooya, K.M.Pohl, M.Bilello, L.Cirillo, G.Biros, E.R.Melhem, C.Davatzikos, "GLISTR: glioma image segmentation and registration", IEEE Trans Med Imaging. 31(10):1941-54, 2012, DOI:10.1109/TMI.2012.2210558
    3. -D.Kwon, R.T.Shinohara, H.Akbari, C.Davatzikos, "Combining Generative Models for Multifocal Glioma Segmentation and Registration", Med Image Comput Comput Assist Interv. 17(Pt 1):763-70, 2014. DOI:10.1007/978-3-319-10404-1_95
      4. +D.Kwon, R.T.Shinohara, H.Akbari, C.Davatzikos, "Combining Generative Models for Multifocal Glioma Segmentation and Registration", Med Image Comput Comput Assist Interv. 17(Pt 1):763-70, 2014, DOI:10.1007/978-3-319-10404-1_95
    4. S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "Segmentation of gliomas in multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework", In Proc. Multimodal Brain Tumor Image Segmentation (BraTS) Challenge. 4:5-12, 2015.
    5. -S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "GLISTRboost: combining multimodal MRI segmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation", Brainlesion (2015). 9556:144-155, 2016. DOI:10.1007/978-3-319-30858-6_1
      6. +S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "GLISTRboost: combining multimodal MRI segmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation", Brainlesion (2015). 9556:144-155, 2016, DOI:10.1007/978-3-319-30858-6_1
    6. -S.Bakas, H.Akbari, A.Sotiras, M.Bilello, M.Rozycki, J.Kirby, J.Freymann, K.Farahani, C.Davatzikos, "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data 4:170117, 2017. DOI:10.1038/sdata.2017.117
      7. +S.Bakas, H.Akbari, A.Sotiras, M.Bilello, M.Rozycki, J.Kirby, J.Freymann, K.Farahani, C.Davatzikos, "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data 4:170117, 2017, DOI:10.1038/sdata.2017.117
    7. -D.Kwon, M.Niethammer, H.Akbari, M.Bilello, C.Davatzikos, K.M.Pohl, "PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration", IEEE Trans Med Imaging. 33(3):651-667, 2014. DOI:10.1109/TMI.2013.2293478
      8. +D.Kwon, M.Niethammer, H.Akbari, M.Bilello, C.Davatzikos, K.M.Pohl, "PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration", IEEE Trans Med Imaging. 33(3):651-667, 2014, DOI:10.1109/TMI.2013.2293478
    diff --git a/docs/gs_supportedImages.html b/docs/gs_supportedImages.html index c500853e4..cd0890eb7 100644 --- a/docs/gs_supportedImages.html +++ b/docs/gs_supportedImages.html @@ -98,9 +98,9 @@

    References:

    1. -J.M.Soares, P.Marques, V.Alves, N.Sousa. "A hitchhiker's guide to diffusion tensor imaging", Front Neurosci. 7:31, 2013. DOI: 10.3389/fnins.2013.00031
      2. +J.M.Soares, P.Marques, V.Alves, N.Sousa. "A hitchhiker's guide to diffusion tensor imaging", Front Neurosci. 7:31, 2013, DOI: 10.3389/fnins.2013.00031
    2. -E.S.Paulson, K.M.Schmainda, "Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors", Radiology. 249(2):601-613, 2008. DOI: 10.1148/radiol.2492071659
      3. +E.S.Paulson, K.M.Schmainda, "Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors", Radiology. 249(2):601-613, 2008, DOI: 10.1148/radiol.2492071659
    diff --git a/docs/index.html b/docs/index.html index 3a9a204e9..2e50b7922 100644 --- a/docs/index.html +++ b/docs/index.html @@ -81,17 +81,31 @@

    Frequently Asked Questions (FAQ)

    Please see our FAQ Section.

    +Citations

    +

    Please make sure that whenever you use and/or refer to CaPTk in your research, you should always cite the following papers:

    +
      +
    • C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018, DOI:10.1117/1.JMI.5.1.011018
      • +
    • S.Pati, A.Singh, S.Rathore, A.Gastounioti, M.Bergman, P.Ngo, S.M.Ha, D.Bounias, J.Minock, G.Murphy, H.Li, A.Bhattarai, A.Wolf, P.Sridaran, R.Kalarot, H.Akbari, A.Sotiras, S.P.Thakur, R.Verma, R.T.Shinohara, P.Yushkevich, Y.Fan, D.Kontos, C.Davatzikos, S.Bakas, "The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview", Springer - BrainLes 2019 - LNCS, Vol.11993, 380-394, 2020, DOI:10.1007/978-3-030-46643-5_38
      • +
    +

    In addition, if the journal/conference where you submit your paper does not restrict you from citing abstracts you might also cite the following:

    +
      +
    • RRID: SCR_017323
      • +
    • S.Rathore, S.Bakas, S.Pati, H.Akbari, R.Kalarot, P.Sridharan, M.Rozycki, M.Bergman, B.Tunc, R.Verma, M.Bilello, C.Davatzikos. "Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma", BrainLes 2017. LNCS Springer, 10670:133-145, 2017, DOI:10.1007/978-3-319-75238-9_12
      • +
    • S.Pati, S.Bakas, A.Sotiras, R.Kalarot, P.Sridharan, M.Bergman, S.Rathore, H.Akbari, P.Yushkevich, T.Shinohara, Y.Fan, D.Kontos, R.Verma, C.Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26-Dec.1, 2017, Chicago IL.
      • +
    • S.Pati, S.Rathore, R.Kalarot, P.Sridharan, M.Bergman, T.Shinohara, P.Yushkevich, Y.Fan, R.Verma, D.Kontos, C.Davatzikos. "Cancer and Phenomics Toolkit (CaPTk): A Software Suite for Computational Oncology and Radiomics", 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.27-Dec.2, 2016, Chicago IL. archive.rsna.org/2016/16014589.html
      • +
    +

    Supporting Grant

    This work is supported by the NIH/NCI/ITCR* grant U24-CA189523.
    * National Institutes of Health / National Cancer Institute / Informatics Technology for Cancer Research

    -

    +

    Disclaimer

    • The software has been designed for research purposes only and has neither been reviewed nor approved for clinical use by the Food and Drug Administration (FDA) or by any other federal/state agency.
    • This code (excluding dependent libraries) is governed by the license provided in https://www.med.upenn.edu/sbia/software-agreement.html unless otherwise specified.
    • The minimum recommended resolution is 1024x768. We have seen some issues with high DPI screens and bug reports related to it will be appreciated.
    -

    +

    Contact

    For more information, please contact software@cbica.upenn.edu.

    diff --git a/docs/index.qhp b/docs/index.qhp index 8bda4c962..33585a51a 100644 --- a/docs/index.qhp +++ b/docs/index.qhp @@ -9,9 +9,10 @@
    -
    -
    -
    +
    +
    +
    +
    diff --git a/docs/libraPapers.html b/docs/libraPapers.html index 9fad9fb49..a110722f4 100644 --- a/docs/libraPapers.html +++ b/docs/libraPapers.html @@ -73,13 +73,13 @@

    References:

    • -A.Gastounioti, A.Oustimov, B.M.Keller, L.Pantalone, M.K.Hsieh, E.F.Conant, D.Kontos. "Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations", Med Phys. 43(11):5862-77, 2016. DOI: 10.1118/1.4963810
      • +A.Gastounioti, A.Oustimov, B.M.Keller, L.Pantalone, M.K.Hsieh, E.F.Conant, D.Kontos. "Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations", Med Phys. 43(11):5862-77, 2016, DOI: 10.1118/1.4963810
    • -A.M.McCarthy, B.M.Keller, L.M.Pantalone, M.K.Hsieh, M.Synnestvedt, E.F.Conant, K.Armstrong, D.Kontos. "Racial differences in quantitative measures of area and volumetric breast density", J Natl Cancer Inst. 108(10), 2016. DOI:10.1093/jnci/djw104
      • +A.M.McCarthy, B.M.Keller, L.M.Pantalone, M.K.Hsieh, M.Synnestvedt, E.F.Conant, K.Armstrong, D.Kontos. "Racial differences in quantitative measures of area and volumetric breast density", J Natl Cancer Inst. 108(10), 2016, DOI:10.1093/jnci/djw104
    • -E.F.Conant, B.M.Keller, L.Pantalone, A.Gastounioti, E.S.McDonald, D.Kontos. "Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures", Radiology. 283(3):673-80, 2017. DOI:10.1148/radiol.2016161286
      • +E.F.Conant, B.M.Keller, L.Pantalone, A.Gastounioti, E.S.McDonald, D.Kontos. "Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures", Radiology. 283(3):673-80, 2017, DOI:10.1148/radiol.2016161286
    • -A.D.Williams, A.So, M.Synnestvedt, C.M.Tewksbury, D.Kontos, M.K.Hsieh, L.Pantalone, E.F.Conant, M.Schnall, K.Dumon, N.Williams, J.Tchou. "Mammographic breast density decreases after bariatric surgery" Breast Cancer Res Treat. 165(3):565-572, 2017. DOI:10.1007/s10549-017-4361-y
      • +A.D.Williams, A.So, M.Synnestvedt, C.M.Tewksbury, D.Kontos, M.K.Hsieh, L.Pantalone, E.F.Conant, M.Schnall, K.Dumon, N.Williams, J.Tchou. "Mammographic breast density decreases after bariatric surgery" Breast Cancer Res Treat. 165(3):565-572, 2017, DOI:10.1007/s10549-017-4361-y
    diff --git a/docs/navtreedata.js b/docs/navtreedata.js index adb0d75e5..9e588504e 100644 --- a/docs/navtreedata.js +++ b/docs/navtreedata.js @@ -35,9 +35,10 @@ var NAVTREE = [ "Coordinate definition (Seed-point initialization)", "Getting_Started.html#gs_seedpoints", [ [ "Bug Tracker and Feature Request", "index.html#autotoc_md136", null ], [ "Frequently Asked Questions (FAQ)", "index.html#autotoc_md137", null ], - [ "Supporting Grant", "index.html#autotoc_md138", null ], - [ "Disclaimer", "index.html#autotoc_md139", null ], - [ "Contact", "index.html#autotoc_md140", null ], + [ "Citations", "index.html#autotoc_md138", null ], + [ "Supporting Grant", "index.html#autotoc_md139", null ], + [ "Disclaimer", "index.html#autotoc_md140", null ], + [ "Contact", "index.html#autotoc_md141", null ], [ "Tumor Points", "Getting_Started.html#gs_seedpoints_tumor", null ], [ "Tissue Points", "Getting_Started.html#gs_seedpoints_tissue", null ] ] ], diff --git a/docs/navtreeindex0.js b/docs/navtreeindex0.js index 6cc5204db..6e341ba37 100644 --- a/docs/navtreeindex0.js +++ b/docs/navtreeindex0.js @@ -27,8 +27,8 @@ var NAVTREEINDEX0 = "Getting_Started.html#gs_keyboard":[1,7], "Getting_Started.html#gs_preprocessing":[1,9], "Getting_Started.html#gs_seedpoints":[1,5], -"Getting_Started.html#gs_seedpoints_tissue":[1,5,6], -"Getting_Started.html#gs_seedpoints_tumor":[1,5,5], +"Getting_Started.html#gs_seedpoints_tissue":[1,5,7], +"Getting_Started.html#gs_seedpoints_tumor":[1,5,6], "Getting_Started.html#gs_segmentation":[1,10], "Getting_Started.html#gs_specializedApps":[1,12], "Getting_Started.html#gs_supportedImages":[1,1], @@ -71,6 +71,7 @@ var NAVTREEINDEX0 = "index.html#autotoc_md138":[2], "index.html#autotoc_md139":[3], "index.html#autotoc_md140":[4], +"index.html#autotoc_md141":[5], "pages.html":[], "preprocessing_bias.html":[3,0,3], "preprocessing_brats.html":[3,0,6], diff --git a/docs/phiEstimator.html b/docs/phiEstimator.html index 6b4a70d9d..7ed0de72d 100644 --- a/docs/phiEstimator.html +++ b/docs/phiEstimator.html @@ -73,11 +73,11 @@

    References:

    • -S.Bakas, H.Akbari, J.Pisapia, M.Martinez-Lage, M.Rozycki, S.Rathore, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the phi-index", Clin Cancer Res. 23(16):4724-4734, 2017. DOI:10.1158/1078-0432.CCR-16-1871
      • +S.Bakas, H.Akbari, J.Pisapia, M.Martinez-Lage, M.Rozycki, S.Rathore, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the phi-index", Clin Cancer Res. 23(16):4724-4734, 2017, DOI:10.1158/1078-0432.CCR-16-1871
    • -S.Bakas, H.Akbari, J.Pisapia, M.Rozycki, D.M.O'Rourke, C.Davatzikos. "Identification of Imaging Signatures of the Epidermal Growth Factor Receptor Variant III (EGFRvIII) in Glioblastoma", Neuro Oncol. 17(Suppl 5):v154, 2015. DOI:10.1093/neuonc/nov225.05
      • +S.Bakas, H.Akbari, J.Pisapia, M.Rozycki, D.M.O'Rourke, C.Davatzikos. "Identification of Imaging Signatures of the Epidermal Growth Factor Receptor Variant III (EGFRvIII) in Glioblastoma", Neuro Oncol. 17(Suppl 5):v154, 2015, DOI:10.1093/neuonc/nov225.05
    • -S.Bakas, Z.A.Binder, H.Akbari, M.Martinez-Lage, M.Rozycki, J.J.D.Morrissette, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "Highly-expressed wild-type EGFR and EGFRvIII mutant glioblastomas have similar MRI signature, consistent with deep peritumoral infiltration", Neuro Oncol. 18(Suppl 6):vi125-vi126, 2016. DOI:10.1093/neuonc/now212.523
      • +S.Bakas, Z.A.Binder, H.Akbari, M.Martinez-Lage, M.Rozycki, J.J.D.Morrissette, N.Dahmane, D.M.O'Rourke, C.Davatzikos, "Highly-expressed wild-type EGFR and EGFRvIII mutant glioblastomas have similar MRI signature, consistent with deep peritumoral infiltration", Neuro Oncol. 18(Suppl 6):vi125-vi126, 2016, DOI:10.1093/neuonc/now212.523
    diff --git a/docs/preprocessing_bias.html b/docs/preprocessing_bias.html index d4dae3286..198f17c69 100644 --- a/docs/preprocessing_bias.html +++ b/docs/preprocessing_bias.html @@ -77,8 +77,8 @@

    Reference:

      -
    1. J.G.Sled, A.P.Zijdenbos, A.C.Evans. "A nonparametric method for automatic correction of intensity nonuniformity in MRI data" IEEE Trans Med Imaging. 17(1):87-97, 1998.
      2. -
    2. N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee. "N4ITK: improved N3 bias correction." IEEE transactions on medical imaging 29.6 (2010): 1310-1320.
      3. +
    3. J.G.Sled, A.P.Zijdenbos, A.C.Evans. "A nonparametric method for automatic correction of intensity nonuniformity in MRI data", IEEE Trans Med Imaging. 17(1):87-97, 1998, DOI:10.1109/42.668698
      4. +
    4. N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee. "N4ITK: improved N3 bias correction." IEEE Trans Med Imaging. 29(6): 1310-1320, 2010, DOI:10.1109/TMI.2010.2046908
    diff --git a/docs/preprocessing_brats.html b/docs/preprocessing_brats.html index 21c66eb97..a85bdbb73 100644 --- a/docs/preprocessing_brats.html +++ b/docs/preprocessing_brats.html @@ -80,10 +80,10 @@

    Reference:

      -
    1. B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694
      2. -
    2. S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI: 10.1038/sdata.2017.117
      3. +
    3. B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI:10.1109/TMI.2014.2377694
      4. +
    4. S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI:10.1038/sdata.2017.117
    5. S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge", arXiv preprint arXiv:1811.02629 (2018)
      6. -
    6. T.Rohlfing, N.M.Zahr, E.V.Sullivan,A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human brain mapping, 31(5), pp.798-819. (2010)
      7. +
    7. T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010, DOI:10.1002/hbm.20906
    diff --git a/docs/preprocessing_histoMatch.html b/docs/preprocessing_histoMatch.html index 774d33ea6..ecd044d27 100644 --- a/docs/preprocessing_histoMatch.html +++ b/docs/preprocessing_histoMatch.html @@ -76,7 +76,7 @@

    Reference:

      -
    1. L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000. DOI:10.1109/42.836373
      2. +
    2. L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000, DOI:10.1109/42.836373
    diff --git a/docs/preprocessing_reg.html b/docs/preprocessing_reg.html index e6a182e41..3bcb8b3f5 100644 --- a/docs/preprocessing_reg.html +++ b/docs/preprocessing_reg.html @@ -79,7 +79,7 @@

    References:

      -
    1. P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI, Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127
      2. +
    2. P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI", Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127, DOI:10.1016/j.jalz.2016.06.205
    3. www.github.com/pyushkevich/greedy
    diff --git a/docs/preprocessing_susan.html b/docs/preprocessing_susan.html index 7e532b838..c61c7b3f3 100644 --- a/docs/preprocessing_susan.html +++ b/docs/preprocessing_susan.html @@ -77,7 +77,7 @@

    Reference:

      -
    1. S.M.Smith, J.M.Brady. "SUSAN-A new approach to low level image processing", International Journal of Computer Vision. 23(1):45-78, 1997.
      2. +
    2. S.M.Smith, J.M.Brady. "SUSAN-A new approach to low level image processing", International Journal of Computer Vision. 23(1):45-78, 1997, DOI:10.1023/A:1007963824710
    diff --git a/docs/preprocessing_zScoreNorm.html b/docs/preprocessing_zScoreNorm.html index b3e5fe515..97f93a22a 100644 --- a/docs/preprocessing_zScoreNorm.html +++ b/docs/preprocessing_zScoreNorm.html @@ -78,7 +78,7 @@

    Reference:

      -
    1. T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010. DOI:10.1002/hbm.20906
      2. +
    2. T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010, DOI:10.1002/hbm.20906
    diff --git a/docs/recurrencePredictor.html b/docs/recurrencePredictor.html index 0d0ee4076..20c13cb9f 100644 --- a/docs/recurrencePredictor.html +++ b/docs/recurrencePredictor.html @@ -73,9 +73,9 @@

    References:

    • -H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. "Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma", Neurosurgery. 78(4):572-80, 2016. DOI:10.1227/NEU.0000000000001202
      • +H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. "Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma", Neurosurgery. 78(4):572-80, 2016, DOI:10.1227/NEU.0000000000001202
    • -H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014. DOI:10.1148/radiol.14132458
      • +H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014, DOI:10.1148/radiol.14132458
    • H.Akbari, L.Macyszyn, J.Pisapia, X.Da, M.Attiah, Y.Bi, S.Pal, R.Davuluri, L.Roccograndi, N.Dahmane, R.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos, "Survival Prediction in Glioblastoma Patients Using Multi-parametric MRI Biomarkers and Machine Learning Methods", American Society of Neuroradiology, O-525:2042-2044, 2015.
    diff --git a/docs/seg_DL.html b/docs/seg_DL.html index c5b650a63..bab468cd8 100644 --- a/docs/seg_DL.html +++ b/docs/seg_DL.html @@ -88,7 +88,7 @@
    1. K.Kamnitsas, C.Ledig, V.F.J.Newcombe, J.P.Simpson, A.D.Kane, D.K.Menon, D.Rueckert, B.Glocker, "Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation", Medical Image Analysis, 2016.
    2. K.Kamnitsas, L.Chen, C.Ledig, D.Rueckert, B.Glocker, "Multi-Scale 3D CNNs for segmentation of brain Lesions in multi-modal MRI", in proceeding of ISLES challenge, MICCAI 2015.
      3. -
    3. S.P.Thakur, J.Doshi, S.Pati, S.M.Ha, C.Sako, S.Talbar, U.Kulkarni, C.Davatzikos, G.Erus, S.Bakas, "Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning", Springer - BrainLes 2019 - LNCS, Vol.11992, 57-68, 2020. DOI: 10.1007/978-3-030-46640-4_6
      4. +
    4. S.P.Thakur, J.Doshi, S.Pati, S.M.Ha, C.Sako, S.Talbar, U.Kulkarni, C.Davatzikos, G.Erus, S.Bakas, "Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning", Springer - BrainLes 2019 - LNCS, Vol.11992, 57-68, 2020, DOI: 10.1007/978-3-030-46640-4_6
    5. S.P.Thakur, J.Doshi, S.Pati, S.M.Ha, C.Sako, S.Talbar, U.Kulkarni, C.Davatzikos, G.Erus, S.Bakas, "Brain Extraction on MRI Scans in Presence of Diffuse Glioma: Multi-institutional Performance Evaluation of Deep Learning Methods and Robust Modality-Agnostic Training", NeuroImage 2020 [Accepted]
    diff --git a/docs/seg_GeoTrain.html b/docs/seg_GeoTrain.html index c528f8adb..134f59fbb 100644 --- a/docs/seg_GeoTrain.html +++ b/docs/seg_GeoTrain.html @@ -79,7 +79,7 @@

    References:

      -
    1. B.Gaonkar, L.Shu, G.Hermosillo, Y.Zhan, "Adaptive geodesic transform for segmentation of vertebrae on CT images", Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 9035:16, 2014. DOI:10.1117/12.2043527.
      2. +
    2. B.Gaonkar, L.Shu, G.Hermosillo, Y.Zhan, "Adaptive geodesic transform for segmentation of vertebrae on CT images", Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 9035:16, 2014, DOI:10.1117/12.2043527.
    diff --git a/docs/seg_SNAP.html b/docs/seg_SNAP.html index 735ebca03..53c0dd5a7 100644 --- a/docs/seg_SNAP.html +++ b/docs/seg_SNAP.html @@ -66,7 +66,7 @@

    References:

      -
    1. P.Yushkevich, Y.Gao, G.Gerig, "ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images", Conf Proc IEEE Eng Med Biol Soc. 2016:3342-3345, 2016. DOI:10.1109/EMBC.2016.7591443.
      2. +
    2. P.Yushkevich, Y.Gao, G.Gerig, "ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images", Conf Proc IEEE Eng Med Biol Soc. 2016:3342-3345, 2016, DOI:10.1109/EMBC.2016.7591443.
    diff --git a/docs/survivalPredictor.html b/docs/survivalPredictor.html index e77ad876a..75366247d 100644 --- a/docs/survivalPredictor.html +++ b/docs/survivalPredictor.html @@ -73,7 +73,7 @@

    Reference:

    • -L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016. DOI:10.1093/neuonc/nov127
      • +L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016, DOI:10.1093/neuonc/nov127
    From b9b20243bc4c6579007657fe9be3f781e06cebf5 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Mon, 29 Jun 2020 15:30:45 -0400 Subject: [PATCH 22/33] added a special case for sensitivity --- src/cbica_toolkit/src/cbicaITKUtilities.h | 29 ++++++++++++++--------- 1 file changed, 18 insertions(+), 11 deletions(-) diff --git a/src/cbica_toolkit/src/cbicaITKUtilities.h b/src/cbica_toolkit/src/cbicaITKUtilities.h index a6606a9d5..16037a80e 100644 --- a/src/cbica_toolkit/src/cbicaITKUtilities.h +++ b/src/cbica_toolkit/src/cbicaITKUtilities.h @@ -1917,6 +1917,16 @@ namespace cbica auto imageToCompare_1 = region.second; auto imageToCompare_2 = regionsToCompare_2[labelString]; + // in case one of the labels is missing, just put something + auto stats_1 = itk::StatisticsImageFilter< TImageType >::New(); + stats_1->SetInput(imageToCompare_1); + stats_1->Update(); + auto max_1 = stats_1->GetMaximum(); + auto stats_2 = itk::StatisticsImageFilter< TImageType >::New(); + stats_2->SetInput(imageToCompare_2); + stats_2->Update(); + auto max_2 = stats_2->GetMaximum(); + auto similarityFilter = itk::LabelOverlapMeasuresImageFilter< TImageType >::New(); similarityFilter->SetSourceImage(imageToCompare_1); @@ -1942,16 +1952,6 @@ namespace cbica returnMap[labelString][metric.first] = metric.second; } - // in case one of the labels is missing, just put something - auto stats_1 = itk::StatisticsImageFilter< TImageType >::New(); - stats_1->SetInput(imageToCompare_1); - stats_1->Update(); - auto max_1 = stats_1->GetMaximum(); - auto stats_2 = itk::StatisticsImageFilter< TImageType >::New(); - stats_2->SetInput(imageToCompare_2); - stats_2->Update(); - auto max_2 = stats_2->GetMaximum(); - if ((max_1 == 0) && (max_2 == 0)) { returnMap[labelString]["Sensitivity"] = 1; @@ -1959,7 +1959,14 @@ namespace cbica } if (std::isnan(returnMap[labelString]["Sensitivity"])) { - returnMap[labelString]["Sensitivity"] = 1; + if (max_1 != max_2) + { + returnMap[labelString]["Sensitivity"] = 0; + } + else + { + returnMap[labelString]["Sensitivity"] = 1; + } } if (std::isinf(returnMap[labelString]["Sensitivity"])) { From 061913d84c633348421c37e8bff1f191c78b360d Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Mon, 29 Jun 2020 15:31:05 -0400 Subject: [PATCH 23/33] version revert --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index b49a5ad58..71d6a28d4 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -13,7 +13,7 @@ SET( ${PROJECT_NAME}_Variant "Full" ) # the particular variant of CaPTk (Full/Ne SET( PROJECT_VERSION_MAJOR 1 ) SET( PROJECT_VERSION_MINOR 8 ) -SET( PROJECT_VERSION_PATCH 0.Alpha2 ) +SET( PROJECT_VERSION_PATCH 0.nonRelease ) SET( PROJECT_VERSION_TWEAK ) # check for the string "nonRelease" in the PROJECT_VERSION_PATCH variable From 8b3a707a5daefeab2f06f270030ee3f037166ed3 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Tue, 30 Jun 2020 08:52:20 -0400 Subject: [PATCH 24/33] recursive delete --- src/cbica_toolkit/src/cbicaITKUtilities.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cbica_toolkit/src/cbicaITKUtilities.h b/src/cbica_toolkit/src/cbicaITKUtilities.h index 16037a80e..195c7bad9 100644 --- a/src/cbica_toolkit/src/cbicaITKUtilities.h +++ b/src/cbica_toolkit/src/cbicaITKUtilities.h @@ -2053,7 +2053,7 @@ namespace cbica result.pop_back(); result.pop_back(); returnMap[labelString]["Hausdorff95"] = std::atof(result.c_str()); - cbica::deleteDir(tempDir); + cbica::removeDirectoryRecursively(tempDir, true); } // in case a label is not defined, use the longest diagonal if (std::isnan(returnMap[labelString]["Hausdorff95"]) || std::isinf(returnMap[labelString]["Hausdorff95"])) From 3d40a503f1028ad46769c0d48c74f7653b33c516 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Tue, 30 Jun 2020 09:49:39 -0400 Subject: [PATCH 25/33] weird bug when an empty label gets passed --- src/cbica_toolkit/src/cbicaITKUtilities.h | 257 +++++++++++----------- 1 file changed, 130 insertions(+), 127 deletions(-) diff --git a/src/cbica_toolkit/src/cbicaITKUtilities.h b/src/cbica_toolkit/src/cbicaITKUtilities.h index 195c7bad9..90cbddcf2 100644 --- a/src/cbica_toolkit/src/cbicaITKUtilities.h +++ b/src/cbica_toolkit/src/cbicaITKUtilities.h @@ -1913,125 +1913,127 @@ namespace cbica for (const auto ®ion : regionsToCompare_1) { auto labelString = region.first; // the label for stats - // get the images to compare up front - auto imageToCompare_1 = region.second; - auto imageToCompare_2 = regionsToCompare_2[labelString]; - - // in case one of the labels is missing, just put something - auto stats_1 = itk::StatisticsImageFilter< TImageType >::New(); - stats_1->SetInput(imageToCompare_1); - stats_1->Update(); - auto max_1 = stats_1->GetMaximum(); - auto stats_2 = itk::StatisticsImageFilter< TImageType >::New(); - stats_2->SetInput(imageToCompare_2); - stats_2->Update(); - auto max_2 = stats_2->GetMaximum(); - - auto similarityFilter = itk::LabelOverlapMeasuresImageFilter< TImageType >::New(); - - similarityFilter->SetSourceImage(imageToCompare_1); - similarityFilter->SetTargetImage(imageToCompare_2); - similarityFilter->Update(); - - returnMap[labelString]["Overlap"] = similarityFilter->GetTotalOverlap(); - returnMap[labelString]["Jaccard"] = similarityFilter->GetUnionOverlap(); - returnMap[labelString]["Dice"] = similarityFilter->GetMeanOverlap(); - if (std::isinf(returnMap[labelString]["Dice"])) + if (!labelString.empty()) { - // this happens in the case where there is a label missing in both the reference and input annotations - returnMap[labelString]["Dice"] = 1; - } - returnMap[labelString]["VolumeSimilarity"] = similarityFilter->GetVolumeSimilarity(); - returnMap[labelString]["FalseNegativeError"] = similarityFilter->GetFalseNegativeError(); - returnMap[labelString]["FalsePositiveError"] = similarityFilter->GetFalsePositiveError(); + // get the images to compare up front + auto imageToCompare_1 = region.second; + auto imageToCompare_2 = regionsToCompare_2[labelString]; + + // in case one of the labels is missing, just put something + auto stats_1 = itk::StatisticsImageFilter< TImageType >::New(); + stats_1->SetInput(imageToCompare_1); + stats_1->Update(); + auto max_1 = stats_1->GetMaximum(); + auto stats_2 = itk::StatisticsImageFilter< TImageType >::New(); + stats_2->SetInput(imageToCompare_2); + stats_2->Update(); + auto max_2 = stats_2->GetMaximum(); + + auto similarityFilter = itk::LabelOverlapMeasuresImageFilter< TImageType >::New(); + + similarityFilter->SetSourceImage(imageToCompare_1); + similarityFilter->SetTargetImage(imageToCompare_2); + similarityFilter->Update(); + + returnMap[labelString]["Overlap"] = similarityFilter->GetTotalOverlap(); + returnMap[labelString]["Jaccard"] = similarityFilter->GetUnionOverlap(); + returnMap[labelString]["Dice"] = similarityFilter->GetMeanOverlap(); + if (std::isinf(returnMap[labelString]["Dice"])) + { + // this happens in the case where there is a label missing in both the reference and input annotations + returnMap[labelString]["Dice"] = 1; + } + returnMap[labelString]["VolumeSimilarity"] = similarityFilter->GetVolumeSimilarity(); + returnMap[labelString]["FalseNegativeError"] = similarityFilter->GetFalseNegativeError(); + returnMap[labelString]["FalsePositiveError"] = similarityFilter->GetFalsePositiveError(); - auto temp_roc = GetSensitivityAndSpecificity< TImageType >(imageToCompare_1, imageToCompare_2); + auto temp_roc = GetSensitivityAndSpecificity< TImageType >(imageToCompare_1, imageToCompare_2); - for (const auto &metric : temp_roc) - { - returnMap[labelString][metric.first] = metric.second; - } + for (const auto &metric : temp_roc) + { + returnMap[labelString][metric.first] = metric.second; + } - if ((max_1 == 0) && (max_2 == 0)) - { - returnMap[labelString]["Sensitivity"] = 1; - returnMap[labelString]["Specificity"] = 1; - } - if (std::isnan(returnMap[labelString]["Sensitivity"])) - { - if (max_1 != max_2) + if ((max_1 == 0) && (max_2 == 0)) { - returnMap[labelString]["Sensitivity"] = 0; + returnMap[labelString]["Sensitivity"] = 1; + returnMap[labelString]["Specificity"] = 1; } - else + if (std::isnan(returnMap[labelString]["Sensitivity"])) + { + if (max_1 != max_2) + { + returnMap[labelString]["Sensitivity"] = 0; + } + else + { + returnMap[labelString]["Sensitivity"] = 1; + } + } + if (std::isinf(returnMap[labelString]["Sensitivity"])) { returnMap[labelString]["Sensitivity"] = 1; } - } - if (std::isinf(returnMap[labelString]["Sensitivity"])) - { - returnMap[labelString]["Sensitivity"] = 1; - } - /// not used till implementation gets standardized - //returnMap[labelString]["Hausdorff95"] = GetHausdorffDistance< TImageType >(imageToCompare_1, imageToCompare_2, 0.95); - //returnMap[labelString]["Hausdorff99"] = GetHausdorffDistance< TImageType >(imageToCompare_1, imageToCompare_2, 0.99); - bool hausdorffFound = true; - std::string hausdorffExe = cbica::getExecutablePath() + "/Hausdorff95" -#if WIN32 - + ".exe" -#endif - ; - if (!cbica::isFile(hausdorffExe)) - { - hausdorffExe = cbica::getExecutablePath() + "../hausdorff95/Hausdorff95" + /// not used till implementation gets standardized + //returnMap[labelString]["Hausdorff95"] = GetHausdorffDistance< TImageType >(imageToCompare_1, imageToCompare_2, 0.95); + //returnMap[labelString]["Hausdorff99"] = GetHausdorffDistance< TImageType >(imageToCompare_1, imageToCompare_2, 0.99); + bool hausdorffFound = true; + std::string hausdorffExe = cbica::getExecutablePath() + "/Hausdorff95" #if WIN32 + ".exe" #endif ; if (!cbica::isFile(hausdorffExe)) { - std::cerr << "Could not find Hausdorff95 executable, so not computing this metric.\n"; - hausdorffFound = false; + hausdorffExe = cbica::getExecutablePath() + "../hausdorff95/Hausdorff95" +#if WIN32 + + ".exe" +#endif + ; + if (!cbica::isFile(hausdorffExe)) + { + std::cerr << "Could not find Hausdorff95 executable, so not computing this metric.\n"; + hausdorffFound = false; + } } - } - if (hausdorffFound) - { - if ((max_1 == 0) || (max_2 == 0)) - { - // this is the case where one of the labels is missing - returnMap[labelString]["Hausdorff95"] = NAN; - } - else + if (hausdorffFound) { - auto tempDir = cbica::createTmpDir(); - auto file_1 = tempDir + "/mask_1.nii.gz"; - auto file_2 = tempDir + "/mask_2.nii.gz"; - auto writer = itk::ImageFileWriter< TImageType >::New(); - writer->SetInput(imageToCompare_1); - writer->SetFileName(file_1); - try + if ((max_1 == 0) || (max_2 == 0)) { - writer->Write(); + // this is the case where one of the labels is missing + returnMap[labelString]["Hausdorff95"] = NAN; } - catch (itk::ExceptionObject &e) - { - std::cerr << "Error occurred while trying to write the image '" << file_1 << "': " << e.what() << "\n"; - } - writer->SetInput(imageToCompare_2); - writer->SetFileName(file_2); - try - { - writer->Write(); - } - catch (itk::ExceptionObject &e) + else { - std::cerr << "Error occurred while trying to write the image '" << file_2 << "': " << e.what() << "\n"; - } - std::array< char, 128 > buffer; - std::string result; - FILE *pPipe; + auto tempDir = cbica::createTmpDir(); + auto file_1 = tempDir + "/mask_1.nii.gz"; + auto file_2 = tempDir + "/mask_2.nii.gz"; + auto writer = itk::ImageFileWriter< TImageType >::New(); + writer->SetInput(imageToCompare_1); + writer->SetFileName(file_1); + try + { + writer->Write(); + } + catch (itk::ExceptionObject &e) + { + std::cerr << "Error occurred while trying to write the image '" << file_1 << "': " << e.what() << "\n"; + } + writer->SetInput(imageToCompare_2); + writer->SetFileName(file_2); + try + { + writer->Write(); + } + catch (itk::ExceptionObject &e) + { + std::cerr << "Error occurred while trying to write the image '" << file_2 << "': " << e.what() << "\n"; + } + std::array< char, 128 > buffer; + std::string result; + FILE *pPipe; #if WIN32 #define POPEN _popen #define PCLOSE _pclose @@ -2039,39 +2041,40 @@ namespace cbica #define POPEN popen #define PCLOSE pclose #endif - pPipe = POPEN((hausdorffExe + " -gt " + file_1 + " -m " + file_2).c_str(), "r"); - if (!pPipe) - { - std::cerr << "Couldn't start command.\n"; - } - while (fgets(buffer.data(), 128, pPipe) != NULL) - { - result += buffer.data(); - } - auto returnCode = PCLOSE(pPipe); - // remove "\n" - result.pop_back(); - result.pop_back(); - returnMap[labelString]["Hausdorff95"] = std::atof(result.c_str()); - cbica::removeDirectoryRecursively(tempDir, true); - } - // in case a label is not defined, use the longest diagonal - if (std::isnan(returnMap[labelString]["Hausdorff95"]) || std::isinf(returnMap[labelString]["Hausdorff95"])) - { - // correct prediction for missing label - if ((max_1 == 0) && (max_2 == 0)) - { - returnMap[labelString]["Hausdorff95"] = 0; + pPipe = POPEN((hausdorffExe + " -gt " + file_1 + " -m " + file_2).c_str(), "r"); + if (!pPipe) + { + std::cerr << "Couldn't start command.\n"; + } + while (fgets(buffer.data(), 128, pPipe) != NULL) + { + result += buffer.data(); + } + auto returnCode = PCLOSE(pPipe); + // remove "\n" + result.pop_back(); + result.pop_back(); + returnMap[labelString]["Hausdorff95"] = std::atof(result.c_str()); + cbica::removeDirectoryRecursively(tempDir, true); } - else + // in case a label is not defined, use the longest diagonal + if (std::isnan(returnMap[labelString]["Hausdorff95"]) || std::isinf(returnMap[labelString]["Hausdorff95"])) { - auto size = imageToCompare_1->GetLargestPossibleRegion().GetSize(); - auto diag_plane_squared = std::pow(size[0], 2) + std::pow(size[1], 2); - auto diag_cube = std::sqrt(std::pow(size[2], 2) + diag_plane_squared); - returnMap[labelString]["Hausdorff95"] = diag_cube; + // correct prediction for missing label + if ((max_1 == 0) && (max_2 == 0)) + { + returnMap[labelString]["Hausdorff95"] = 0; + } + else + { + auto size = imageToCompare_1->GetLargestPossibleRegion().GetSize(); + auto diag_plane_squared = std::pow(size[0], 2) + std::pow(size[1], 2); + auto diag_cube = std::sqrt(std::pow(size[2], 2) + diag_plane_squared); + returnMap[labelString]["Hausdorff95"] = diag_cube; + } } - } - } // end hausdorff found + } // end hausdorff found + } // end labelString check } return returnMap; From 1c9d4871761fb007621f503bbd37544756d4bf1e Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Tue, 30 Jun 2020 17:23:26 -0400 Subject: [PATCH 26/33] better output --- src/applications/PseudoProgressionEstimator.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/applications/PseudoProgressionEstimator.cpp b/src/applications/PseudoProgressionEstimator.cpp index c84c690c5..a79e110c0 100644 --- a/src/applications/PseudoProgressionEstimator.cpp +++ b/src/applications/PseudoProgressionEstimator.cpp @@ -928,7 +928,7 @@ VariableSizeMatrixType PseudoProgressionEstimator::LoadPseudoProgressionTestingD for (unsigned int sid = 0; sid < testingsubjects.size(); sid++) { - std::cout << "Loading Remaining Features: " << sid << std::endl; + std::cout << "Loading and processing Feature (testing): " << sid << std::endl; VectorDouble neuroScores; std::map currentsubject = testingsubjects[sid]; @@ -1056,7 +1056,7 @@ VariableSizeMatrixType PseudoProgressionEstimator::LoadPseudoProgressionTestingD otherFeatures[sid][counter] = Features[j]; counter++; } - std::cout << "Counter Size" << counter << std::endl; + std::cout << "Counter Size (testing): " << counter << std::endl; } std::cout << "Basic features copied in the OtherFeatures map." << std::endl; @@ -1366,7 +1366,7 @@ VariableSizeMatrixType PseudoProgressionEstimator::LoadPseudoProgressionTraining for (unsigned int sid = 0; sid < trainingsubjects.size(); sid++) { - std::cout << "Loading Remaining Features: " << sid << std::endl; + std::cout << "Loading and processing Feature (training): " << sid << std::endl; std::map currentsubject = trainingsubjects[sid]; CSVFileReaderType::Pointer reader = CSVFileReaderType::New(); @@ -1480,7 +1480,7 @@ VariableSizeMatrixType PseudoProgressionEstimator::LoadPseudoProgressionTraining otherFeatures[sid][counter] = Features[j]; counter++; } - std::cout << "Counter Size" << counter << std::endl; + std::cout << "Counter Size (training): " << counter << std::endl; } std::cout << "Basic features copied in the OtherFeatures map." << std::endl; From 9ff092885a4fa2b6d7fd440057ea6913aa314fcc Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Tue, 30 Jun 2020 17:24:17 -0400 Subject: [PATCH 27/33] version updated for release --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 71d6a28d4..b49a5ad58 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -13,7 +13,7 @@ SET( ${PROJECT_NAME}_Variant "Full" ) # the particular variant of CaPTk (Full/Ne SET( PROJECT_VERSION_MAJOR 1 ) SET( PROJECT_VERSION_MINOR 8 ) -SET( PROJECT_VERSION_PATCH 0.nonRelease ) +SET( PROJECT_VERSION_PATCH 0.Alpha2 ) SET( PROJECT_VERSION_TWEAK ) # check for the string "nonRelease" in the PROJECT_VERSION_PATCH variable From 7ecd8c839fcab1f1f9f3e83ce894538caee70414 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Wed, 1 Jul 2020 08:51:59 -0400 Subject: [PATCH 28/33] rectifying the "cond" usage in doxygen, which was not working --- 3_HowToGuides.txt | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/3_HowToGuides.txt b/3_HowToGuides.txt index 2ffc00717..5df7279d9 100644 --- a/3_HowToGuides.txt +++ b/3_HowToGuides.txt @@ -800,16 +800,6 @@ This application provides the detection of EGFRvIII mutation status of de nov -# Subject_IDn USAGE: -\cond DOXYGEN_WILL_EXCLUDE_THIS_CONDITIONAL_BLOCK -- Train New Model: - -# "Select Subjects". Select the input directory that follows the folder structure described above. - -# "Output". Select the folder where the trained model will be saved. - -# A pop-up window appears displaying the completion of model building (time depends on the number of patients: ~2*patients minutes). - - This application is also available as with a stand-alone CLI for data analysts to build pipelines around, using the following example command: -\verbatim -${CaPTk_InstallDir}/bin/EGFRvIIIIndexPredictor.exe -t 0 -i C:/EGFRvIIIInputDirectory -o C:/EGFRvIIIInputModel -\endverbatim -\endcond - Use Existing Model -# "Model Directory". Choose the directory of a saved model. From 3741d5702c94dc07acd85b8cdf3a43225d62bc6d Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Wed, 1 Jul 2020 08:52:08 -0400 Subject: [PATCH 29/33] html updates --- docs/Glioblastoma_EGFRvIII.html | 23 +++++++++++++++++++++-- docs/Glioblastoma_Pseudoprogression.html | 21 +-------------------- 2 files changed, 22 insertions(+), 22 deletions(-) diff --git a/docs/Glioblastoma_EGFRvIII.html b/docs/Glioblastoma_EGFRvIII.html index 1b0837b59..607b981bc 100644 --- a/docs/Glioblastoma_EGFRvIII.html +++ b/docs/Glioblastoma_EGFRvIII.html @@ -110,8 +110,27 @@
-

USAGE:

- +

USAGE:

+
    +
  • Use Existing Model
      +
    1. "Model Directory". Choose the directory of a saved model.
      2. +
    2. "Test Subjects". Select the input directory that follows the folder structure described above.
      3. +
    3. "Output". Select the output directory where a .csv file with the mutation status for all patients will be saved, and click on 'Confirm'.The first and the second column of .csv will be subject's ID and distancce of the subject from the hyperplance of EGFRvIII model.
      4. +
    4. A pop-up window appears displaying the completion of result calculation. The window will also show the detected mutation status of the first subject in the Data_of_multiple_patients folder (runtime depends on the number of patients: ~2*patients minutes).
      5. +
    +
      +
    • This application is also available as with a stand-alone CLI for data analysts to build pipelines around, using the following example command: 
      ${CaPTk_InstallDir}/bin/EGFRvIIIIndexPredictor.exe -t 1 -i C:/EGFRvIIIInputDirectory -m C:/EGFRvIIIInputModel -o C:/EGFRvIIIOutputDirectory
+
      • +
    +
    • +
+
+

References:

+
    +
  1. H. Akbari, S. Bakas, J.M. Pisapia, M.P. Nasrallah, M. Rozycki, M. Martinez-Lage, J.J.D. Morrissette, N. Dahmane, D.M.O’Rourke, C. Davatzikos. "In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature", Neuro Oncol. 20(8):1068-1079, 2018
    2. +
+
+ diff --git a/docs/Glioblastoma_Pseudoprogression.html b/docs/Glioblastoma_Pseudoprogression.html index 8e5ef116c..1757f5f53 100644 --- a/docs/Glioblastoma_Pseudoprogression.html +++ b/docs/Glioblastoma_Pseudoprogression.html @@ -61,26 +61,7 @@
Brain Cancer: Pseudoprogression Infiltration Index
-
    -
  • Use Existing Model
      -
    1. "Model Directory". Choose the directory of a saved model.
      2. -
    2. "Test Subjects". Select the input directory that follows the folder structure described above.
      3. -
    3. "Output". Select the output directory where a .csv file with the mutation status for all patients will be saved, and click on 'Confirm'.The first and the second column of .csv will be subject's ID and distancce of the subject from the hyperplance of EGFRvIII model.
      4. -
    4. A pop-up window appears displaying the completion of result calculation. The window will also show the detected mutation status of the first subject in the Data_of_multiple_patients folder (runtime depends on the number of patients: ~2*patients minutes).
      5. -
    -
      -
    • This application is also available as with a stand-alone CLI for data analysts to build pipelines around, using the following example command: 
      ${CaPTk_InstallDir}/bin/EGFRvIIIIndexPredictor.exe -t 1 -i C:/EGFRvIIIInputDirectory -m C:/EGFRvIIIInputModel -o C:/EGFRvIIIOutputDirectory
-
      • -
    -
    • -
-
-

References:

-
    -
  1. H. Akbari, S. Bakas, J.M. Pisapia, M.P. Nasrallah, M. Rozycki, M. Martinez-Lage, J.J.D. Morrissette, N. Dahmane, D.M.O’Rourke, C. Davatzikos. "In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature", Neuro Oncol. 20(8):1068-1079, 2018
    2. -
-
-

This application provides an estimate of the pseudo-progression after radiotherapy in glioblastoma patients, via multi-parametric MRI analysis, as shown in [1].

+

This application provides an estimate of the pseudo-progression after radiotherapy in glioblastoma patients, via multi-parametric MRI analysis, as shown in [1].

REQUIREMENTS:

  1. Co-registered Multimodal MRI: T1, T1-Gd, T2, T2-FLAIR, DSC-4D, DSC-PH, DSC-PSR, DSC-RCBV, DTI-AX, DTI-FA, DTI-RAD, DTI-TR. Ensure that these are the identified modalities in the drop-down menus next to each loaded image.
  2. Segmentation label of the demarcated region of interest (Label=1) in a single NIfTI (.nii.gz) file.
    3. From 0b5acd356cf85fb14c2d0abfd2ec67724d2f9f10 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Wed, 1 Jul 2020 09:42:38 -0400 Subject: [PATCH 30/33] updated test build version --- Readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Readme.md b/Readme.md index abaf41aae..729116027 100644 --- a/Readme.md +++ b/Readme.md @@ -45,7 +45,7 @@ By downloading CaPTk, you agree to our [License](./LICENSE). You can review Inst | macOS | https://www.nitrc.org/frs/downloadlink.php/11650 | | Archive | https://www.nitrc.org/frs/?group_id=1059 | -## Test Build (1.8.0.Alpha) +## Test Build (1.8.0.Alpha2) | Platform (x64) | Link | |:--------------:|:------------------------------------------------:| From 70ee0b3c447fe19b7d58835e1135d3b6e27e8364 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Wed, 1 Jul 2020 10:38:52 -0400 Subject: [PATCH 31/33] making the t1t1ce modality construction consistent for training and testing --- src/applications/PseudoProgressionEstimator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/applications/PseudoProgressionEstimator.cpp b/src/applications/PseudoProgressionEstimator.cpp index 4f7087276..619ced0bf 100644 --- a/src/applications/PseudoProgressionEstimator.cpp +++ b/src/applications/PseudoProgressionEstimator.cpp @@ -1368,7 +1368,7 @@ VariableSizeMatrixType PseudoProgressionEstimator::LoadPseudoProgressionTraining ImageType::Pointer OriginalT1ImagePointer = ReadNiftiImage(static_cast(currentsubject[CAPTK::ImageModalityType::IMAGE_TYPE_T1])); ImageType::Pointer OriginalT2ImagePointer = ReadNiftiImage(static_cast(currentsubject[CAPTK::ImageModalityType::IMAGE_TYPE_T2])); - ImageType::Pointer OriginalT1T1CEImagePointer = MakeAdditionalModality(OriginalT1ImagePointer, OriginalT1CEImagePointer); + ImageType::Pointer OriginalT1T1CEImagePointer = MakeAdditionalModality(OriginalT1CEImagePointer, OriginalT1ImagePointer); ImageType::Pointer OriginalT2FLImagePointer = MakeAdditionalModality(OriginalT2ImagePointer, OriginalT2FlairImagePointer); ImageType::Pointer T1ImagePointer = RescaleImageIntensity(OriginalT1ImagePointer); From 22e94734297ed2e84e69821727f729462caf2097 Mon Sep 17 00:00:00 2001 From: sarthakpati Date: Wed, 1 Jul 2020 12:05:55 -0400 Subject: [PATCH 32/33] changed default save location, fixes #734 --- src/view/gui/ui_fFeaturePanel.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/view/gui/ui_fFeaturePanel.h b/src/view/gui/ui_fFeaturePanel.h index c8df30791..686231dc0 100644 --- a/src/view/gui/ui_fFeaturePanel.h +++ b/src/view/gui/ui_fFeaturePanel.h @@ -318,7 +318,7 @@ class Ui_fFeaturePanel // this is done solely for the reason for saving everything in the tempDir // this folder is deleted after this command to ensure no conflict with m_tempFolderLocation from fMainWindow - m_txtSaveFileName = new QLineEdit(std::string(loggerFolder + "features.csv").c_str()); + m_txtSaveFileName = new QLineEdit(std::string(cbica::getUserHomeDirectory() + "/captk_features.csv").c_str()); m_txtSaveFileName->setAlignment(Qt::AlignCenter | Qt::AlignVCenter); //flLayout->addWidget(m_verticalConcat); From 44fd82dea6e849d1f40fdf05355625dc6d5d6754 Mon Sep 17 00:00:00 2001 From: Sarthak Pati Date: Wed, 1 Jul 2020 18:41:20 -0400 Subject: [PATCH 33/33] dummy commit for azure --- Readme.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Readme.md b/Readme.md index 729116027..1cbfecc95 100644 --- a/Readme.md +++ b/Readme.md @@ -72,11 +72,11 @@ For more information, please contact C ## GitHub Distribution -We currently provide only our tagged versions of the code via GitHub. Check the "tags" using your favorite Git client after cloning our repository. The analogous commands are as follows: +We currently provide only our tagged versions of the code via GitHub. Check the "tags" using your favorite Git client after cloning our repository. The analogous commands are as follows: ```bash git clone https://github.com/cbica/captk.git latesttag=$(git describe --tags) echo checking out ${latesttag} git checkout ${latesttag} -``` \ No newline at end of file +```