initial RTD commit to test things are working

.gitignore

@@ -7,8 +7,8 @@
 ## https://docs.npmjs.com/misc/faq#should-i-check-my-node_modules-folder-into-git
 node_modules
 
-# Book build output
-_book
+# Book and readthedocs build output
+_*
 
 # eBook build output
 *.epub

contributing.md → content/contributing.md

@@ -2,21 +2,13 @@
 
 There are several ways you can contribute.
 
-## Contributing the content
+Contributing the content
 
 You can either follow the instructions for the specific type of object, and submit your responses/screenshots/datasets by email/Dropbox, or you can edit this document directly.
 
 If you want to edit the section for your platform yourself, follow the instructions below:
 
 1. Create an account on [GitBook](https://www.gitbook.com/) \(you can reuse your GitHub login to authenticate\)
-2. Send your login name to Andrey Fedorov, so that he can add you to the list of collaborators. 
+2. Send your login name to Andrey Fedorov, so that he can add you to the list of collaborators.
 3. Take a look at [this video ](https://www.youtube.com/watch?v=-DkV2ainp10)to get started with the editing process.
 
-## Comments/discussion
-
-You can initiate a discussion for a specific paragraph of text. If you mouse over the paragraph while reading [the web version of the book](https://fedorov.gitbooks.io/rsna2016-qirr-dicom4qi) on gitbooks.io, you should see a **"+"** to the right of the paragraph you are reading. You can click it and initiate a new discussion, as shown in the screenshot below.
-
-![](.gitbook/assets/gitbook_comment.png)
-
-Note that you will need to sign in before you can participate in a discussion \(gitbook accepts Facebook, Twitter, Google and Github authentication\).
-

history.md → content/history.md

@@ -1,6 +1,5 @@
-# History
-
-## History
+History
+=======
 
 In 2015, [Andrey Fedorov](http://fedorov.github.io) and [Daniel Rubin](https://med.stanford.edu/profiles/daniel-rubin) came up with an idea of a collaborative project with the goal of testing and improving [DICOM](http://dicom.nema.org/Dicom/about-DICOM.html) interoperability for communicating [quantitative image analysis results](https://peerj.com/articles/2057/) \(so called, "DICOM-QI connectathon"\). This led to a Quantitative Imaging Reading Room \(QIRR\) exhibit at the annual convention of the [Radiological Society of North America \(RSNA\)](http://rsna.org) in 2015. The goal of that exhibit was to demonstrate DICOM-based communication of the image segmentation results. You can see the summary of the exhibit results in [this poster](https://dx.doi.org/10.6084/m9.figshare.1619877.v1).
 
@@ -10,17 +9,20 @@ In 2016, a larger group assembled aiming to continue and expand the scope of the
 
 ![QIICR RSNA 2016 Poster](https://github.com/qiicr/dicom4qi/tree/7ac34ccdf4477ff6e51ff8d5528794fcc4cfd4c9/intro/images/QIICR-RSNA2016-poster.jpg)
 
-## Evolution of the DICOM4QI task set
+Evolution of the DICOM4QI task set
+----------------------------------
 
-### 2015
+2015
+^^^^
 
 The scope of the initial iteration of DICOM4QI included only the DICOM segmentation objects.
 
-### 2016
+2016
+^^^^
 
 Scope extended to include parametric maps and TID1500 structured reports.
 
-### 2017
+2017
+^^^^
 
 Scope extended to include longitudinal datasets and DICOM tractography objects.
-

README.md → content/index.md

@@ -1,6 +1,8 @@
-# Introduction
+Introduction
+============
 
-## Summary
+About
+-------
 
 DICOM4QI is an open demonstration and connectathon with the purpose of evaluating interoperability of the image analysis tools and workstations, applied to exchange of the quantitative image analysis results using DICOM standard.
 
@@ -15,11 +17,28 @@ The present document was created to help with the organization of the exhibit ac
 * First, this is the place to develop and document operating procedures, expectations and organize test datasets.
 * Second, this document will be used to report the connectathon results.
 
-## Notes to contributors to this document
+Notes to contributors to this document
+--------------------------------------
 
-The GitHub repository mirroring the content of this GitBook is located here: [https://github.com/QIICR/rsna2016-qirr-dicom4qi](https://github.com/QIICR/rsna2016-qirr-dicom4qi)
+The GitHub repository mirroring the content of this GitBook is located here: https://github.com/QIICR/DICOM4QI
 
-## References
+References
+----------
 
 * Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, Onken M, Riesmeier J, Pieper S, Kikinis R, Buatti J, Beichel RR. \(2016\) DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ 4:e2057 [https://doi.org/10.7717/peerj.2057](https://doi.org/10.7717/peerj.2057)
 
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   scope
+   participants
+   contributing
+   history
+
+   instructions/instructions
+    instructions/seg
+   instructions/pm
+   instructions/sr-tid1500
+   instructions/tr
+   instructions/longitudinal

instructions/README.md → content/instructions/instructions.md

@@ -1,12 +1,6 @@
-# Instructions for participants
 
-## Overview
 
-The overall idea of the activity is to demonstrate interchange of the types of DICOM objects needed for quantitative imaging research.
-
-As such, we aim to test the capabilities of the individual platforms to consume and produce the types of DICOM objects in question.
-
-**Note: to participate, you do NOT need to support** _**all**_ **of the object types and tasks listed in the following sections!**
+The overall goal is to demonstrate interchange of the types of DICOM objects needed for quantitative imaging research. To participate, you do NOT need to support all of the object types and tasks listed in the following sections!
 
 **To participate, you do not need to be physically present at RSNA!** You can submit your results for inclusion in this public resource. Those of us at RSNA will be happy to tell attendees about your tool, and refer them to you if there are further questions we cannot answer.
 
@@ -27,4 +21,3 @@ Your submission **must** include the details about the platform you used to gene
 **If your platform and usage instructions are publicly available, you have the option to just send those to Andrey Fedorov, and he can do all the testing and populate the document content with the results.**
 
 The details for the individual object types follow in the subsequent sections.
-

instructions/longitudinal-annotation.md → content/instructions/longitudinal.md

@@ -1,12 +1,14 @@
-# Longitudinal annotation
-
-## Overview
+**********************
+Longitudinal annotation
+**********************
 
 In this task we provide a dataset that consists of the two imaging series corresponding to the two time points in visualizing certain imaging finding. Each of these imaging series are accompanied by the volumetric segmentation \(as DICOM SEG\) and segmentation-derived measurements \(as DICOM TID1500 documents\).
 
 The task is to demonstrate how the tool presents to the user the longitudinal aspect of finding measurement.
 
-## Tasks for participants
+**********************
+Tasks for participants
+**********************
 
 1. **Description of the platform/product**:
    * **name and version of the software** used for testing
@@ -23,25 +25,28 @@ The task is to demonstrate how the tool presents to the user the longitudinal as
    * load each of the datasets that accompany the imaging series into your platform
    * submit a screenshot demonstrating the presentation of the loaded measurements to the user by email to Andrey Fedorov
 
-## Test dataset \#1
+Test dataset #1
+===============
 
 This is a dataset encoding the changes in morphology of a lung lesion over two CT imaging sessions. The source of this dataset is the [TCIA RIDER-LungCT collection](https://wiki.cancerimagingarchive.net/display/Public/RIDER+Lung+CT) \(subject RIDER-1500037140\).
 
 Download the ZIP archive containing 2 CT series, 2 SEG series, and 2 SR series [here](http://slicer.kitware.com/midas3/download/item/313148/RIDER-1500037140.zip).
 
 Screenshots below show the location of the lesion, and the measurements stored in the SR objects, as displayed using 3D Slicer QuantitativeReporting extension.
 
-**Time point 1**  
+Time point 1
 
 
 ![](../.gitbook/assets/rider-1500037140-1.jpg)
 
-**Time point 2**  
+Time point 2
+
 
 
 ![](../.gitbook/assets/rider-1500037140-2.jpg)
 
-### Citations
+Citations
+
 
 The CT series from Test dataset \#1 are accompanied by the following citations.
 
@@ -56,4 +61,3 @@ Zhao, B., James, L. P., Moskowitz, C. S., Guo, P., Ginsberg, M. S., Lefkowitz, R
 **TCIA Citation**
 
 Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive \(TCIA\): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. \([paper](http://link.springer.com/article/10.1007%2Fs10278-013-9622-7)\)
-

instructions/pm.md → content/instructions/pm.md

@@ -1,10 +1,10 @@
-# Parametric maps
-
-## Overview
+Parametric maps
+===============
 
 In this task the participants are expected to demonstrate the capability of the tool to handle loading of the DICOM Parametric Map \(DICOM PM\) object.
 
-## Tasks for participants
+Tasks for participants
+----------------------
 
 1. **Description of the platform/product**:
    * **name and version of the software** used for testing
@@ -13,21 +13,22 @@ In this task the participants are expected to demonstrate the capability of the
    * **open source?** if yes - provide a link to source code
    * **what DICOM library do you use?** - if you use certain DICOM toolkit to support this functionality, please list it, if possible
    * **Description of the relevant features of the platform**:
-     * please provide the screenshot of the user interface for the functionality specific to creating/displaying parametric maps 
-     * how do you communicate parametric map semantics to the user \(quantity encoded, units\)? 
+     * please provide the screenshot of the user interface for the functionality specific to creating/displaying parametric maps
+     * how do you communicate parametric map semantics to the user \(quantity encoded, units\)?
 2. **Read task** \(for each dataset!\)
    * load each of the DICOM Parametric map datasets into your platform
    * submit a screenshot demonstrating the presentation of the loaded parametric maps to the user by email to Andrey Fedorov
 
 Note: the screenshots and the DICOM objects you submit will be distributed publicly and included in this document in the Results section.
 
-### Test dataset \#1
+Test dataset #1
+^^^^^^^^^^^^^^^
 
 This is a dataset encoding the Apparent Diffusion Coefficient \(ADC\) map produced by a GE scanner as a DICOM Parametric map object that [can be downloaded here](http://slicer.kitware.com/midas3/download/item/257241/paramap.dcm.zip). The original ADC map [available here](http://slicer.kitware.com/midas3/download/item/126196/701-ADCb500.zip) was saved as an object of MR modality by the scanner software.
 
 This dataset encodes integer-valued pixels, and the ADC units are micrometers per squared second \(as noted in the object\).
 
-### Test dataset \#2
+Test dataset #2
+^^^^^^^^^^^^^^^
 
 This dataset that [can be downloaded here](http://slicer.kitware.com/midas3/download/item/257243/paramap-float.dcm.zip) encodes [the same ADC map as the first dataset](http://slicer.kitware.com/midas3/download/item/126196/701-ADCb500.zip), but in meters per squared second units. The result is an object where each pixel value is less than one. The goal of this object is to test rendering of the true floating point pixels.
-

instructions/seg.md → content/instructions/seg.md

@@ -1,14 +1,14 @@
-# Segmentations \(DICOM SEG\)
-
-## Overview
+Segmentations (DICOM SEG)
+-------------------------
 
 The purpose of this task is to demonstrate support of the DICOM Segmentation Image \(DICOM SEG\) object.
 
 The basic read task involves loading the existing DICOM SEG object, and demonstrating segmentation overlay on the image being annotated.
 
 Write task involves volumetric segmentation of a finding \(evaluation of the precision/accuracy of the segmentation is out of the scope of this demonstration\) and storing the result as a DICOM SEG object.
 
-## Tasks for participants
+Tasks for participants
+----------------------
 
 1. **Description of the platform/product**:
    * **name and version of the software** used for testing
@@ -17,11 +17,11 @@ Write task involves volumetric segmentation of a finding \(evaluation of the pre
    * **open source?** if yes - provide a link to source code
    * **what DICOM library do you use?** - if you use certain DICOM toolkit to support this functionality, please list it, if possible
    * **Description of the relevant features of the platform**:
-     * are both single and multiple segments supported? how are the overlapping segments handled? 
-     * do you support both BINARY and FRACTIONAL segmentation types? 
+     * are both single and multiple segments supported? how are the overlapping segments handled?
+     * do you support both BINARY and FRACTIONAL segmentation types?
      * do you support compressed objects? if yes - for reading, writing, or for both?
-     * do you render the segment using the color specified in the DICOM object? 
-     * how do you communicate segment semantics to the user? 
+     * do you render the segment using the color specified in the DICOM object?
+     * how do you communicate segment semantics to the user?
      * how do you support the user in defining the semantics of the object at the time segmentation is created?
 2. **Read task** \(for each dataset!\)
    * load each of the DICOM SEG datasets that accompany the imaging series into your platform
@@ -36,7 +36,8 @@ Write task involves volumetric segmentation of a finding \(evaluation of the pre
 
 Note: \(1\) we are not assessing the accuracy of lesion segmentation, any method is  good; \(2\) the screenshots and the DICOM SEG objects you submit will be distributed publicly and included in this document in the Results section.
 
-### Test dataset \#1
+Test dataset \#1
+^^^^^^^^^^^^^^^^
 
 The imaging dataset is a chest CT with a single lung lesion located in the right lung lobe. This dataset is subject LIDC-IDRI-0314 from The Cancer Imaging Archive \([TCIA](http://www.cancerimagingarchive.net/)\) [LIDC-IDRI](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI) collection.
 
@@ -50,7 +51,8 @@ Download the zip archive of the CT series [here](http://slicer.kitware.com/midas
 
 Download the DICOM SEG datasets produced by the platforms that already submitted results [here](http://slicer.kitware.com/midas3/folder/3774) \(data is organized in subfolders corresponding to the individual platforms\).
 
-### Test dataset \#2
+Test dataset \#2
+^^^^^^^^^^^^^^^^
 
 The imaging dataset consists of a PET and CT series for subject QIN-HEADNECK-01-0024 from the TCIA [QIN-HEADNECK](https://wiki.cancerimagingarchive.net/display/Public/QIN-HEADNECK) collection. This data set contains two lesions. This allows to test that the platform can handle more than one segment.
 
@@ -64,7 +66,8 @@ Download the zip archive of the CT series [here](http://slicer.kitware.com/midas
 
 Download the DICOM SEG datasets produced by the platforms that already submitted results [here](http://slicer.kitware.com/midas3/folder/3786) \(data is organized in subfolders corresponding to the individual platforms\).
 
-### Test dataset \#3
+Test dataset \#3
+^^^^^^^^^^^^^^^^
 
 The imaging dataset consists of a PET and CT series for subject QIN-HEADNECK-01-0139 from the TCIA [QIN-HEADNECK](https://wiki.cancerimagingarchive.net/display/Public/QIN-HEADNECK) collection. This data set contains 11 lesions. This allows to test that the platform can handle relatively large number of segments.
 
@@ -74,16 +77,14 @@ Download the zip archive of the CT series [here](http://slicer.kitware.com/midas
 
 ![](../.gitbook/assets/qin-headneck-01-0139_screenshot2.png)
 
-
-
-
 ![](../.gitbook/assets/qin-headneck-01-0139_screenshot1.png)
 
 **Segmentation datasets**
 
 Download the DICOM SEG datasets produced by the platforms that already submitted results [here](http://slicer.kitware.com/midas3/folder/3858) \(data is organized in sub-folders corresponding to the individual platforms\).
 
-### Test dataset \#4
+Test dataset \#4
+^^^^^^^^^^^^^^^^
 
 The imaging dataset consists of an MR series for subject QIN-PROSTATE-001 from the TCIA [QIN-PROSTATE](https://wiki.cancerimagingarchive.net/display/Public/QIN+PROSTATE) collection. This data set contains 1 lesion segmentation, and has non-identity orientation.
 
@@ -96,4 +97,3 @@ Download the zip archive of the MR series [here](http://slicer.kitware.com/midas
 **Segmentation datasets**
 
 Download the DICOM SEG datasets produced by the platforms that already submitted results [here](http://slicer.kitware.com/midas3/folder/3888) \(data is organized in subfolders corresponding to the individual platforms\).
-

instructions/sr-tid1500.md → content/instructions/sr-tid1500.md

@@ -1,14 +1,14 @@
-# Measurements \(DICOM SR TID1500\)
-
-## Overview
+Measurements (DICOM SR TID1500)
+===============================
 
 The purpose of this task is to demonstrate support of the DICOM Structured Reporting template TID1500 \(DICOM TID1500\) for storing measurements derived from volumetric segmentations.
 
 The basic read task involves loading the existing DICOM TID1500 object \(ideally, in the context of the source image series and the segmentation used to derived that measurement\), and demonstrating the user interface presenting the loaded measurements.
 
 Write task involves generation of a new DICOM TID1500 dataset for a specified combination of the input image series and the volumetric segmentation defined as DICOM SEG.
 
-## Tasks for participants
+Tasks for participants
+----------------------
 
 1. **Description of the platform/product**:
    * **name and version of the software** used for testing
@@ -17,8 +17,8 @@ Write task involves generation of a new DICOM TID1500 dataset for a specified co
    * **open source?** if yes - provide a link to source code
    * **what DICOM library do you use?** - if you use certain DICOM toolkit to support this functionality, please list it, if possible
    * **Description of the relevant features of the platform**:
-     * please provide the screenshot of the user interface for the functionality specific to creating/displaying measurements 
-     * how do you communicate measurement semantics to the user? 
+     * please provide the screenshot of the user interface for the functionality specific to creating/displaying measurements
+     * how do you communicate measurement semantics to the user?
 2. **Read task** \(for each dataset!\)
    * load each of the DICOM SR datasets that accompany the imaging series into your platform
    * submit a screenshot demonstrating the presentation of the loaded measurements to the user by email to Andrey Fedorov
@@ -29,7 +29,8 @@ Write task involves generation of a new DICOM TID1500 dataset for a specified co
 
 Note: the screenshots and the DICOM objects you submit will be distributed publicly and included in this document in the Results section.
 
-### Test dataset \#1
+Test dataset #1
+^^^^^^^^^^^^^^^
 
 This is a dataset consisting of 3 slices of a [liver CT series](http://slicer.kitware.com/midas3/download/item/257238/liver-3slices-CT.zip), and rough [segmentation of the liver defining ROI](http://slicer.kitware.com/midas3/download/item/257239/liver.dcm) for calculating the measurements.
 
@@ -44,9 +45,9 @@ The measurements stored in the SR dataset are the following:
 * Volume = 70361.9 cubic millimeter
 * Volume = 70.3619 cubic centimeter
 
-### Test dataset \#2
+Test dataset #2
+^^^^^^^^^^^^^^^
 
 [This SR dataset](http://slicer.kitware.com/midas3/download/item/262094/Measurements_User2_SemiAuto_Trial2.dcm) contains measurements over the segmentations of tumor and "hot" lymph nodes in \[SEG Test dataset \#3\)\[[https://fedorov.gitbooks.io/rsna2016-qirr-dicom4qi/content/instructions/seg.html\#test-dataset-3](https://fedorov.gitbooks.io/rsna2016-qirr-dicom4qi/content/instructions/seg.html#test-dataset-3)\].
 
 The types of measurements stored in this object are described in detail in [this article](https://peerj.com/articles/2057/). There is a separate group of measurements for each of the segments in the referenced segmentation object.
-