S3_File ECFC for reb.cppipe

CellProfiler Pipeline: http://www.cellprofiler.org
Version:5
DateRevision:421
GitHash:
ModuleCount:22
HasImagePlaneDetails:False

Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    :
    Filter images?:Images only
    Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\/]\.")

Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Extract metadata?:Yes
    Metadata data type:Text
    Metadata types:{}
    Extraction method count:1
    Metadata extraction method:Extract from file/folder names
    Metadata source:File name
    Regular expression to extract from file name:^(?P<Plate>.*)_(?P<Well>[A-P][0-9]{2})_s(?P<Site>[0-9])_w(?P<ChannelNumber>[0-9])
    Regular expression to extract from folder name:(?P<Date>[0-9]{4}_[0-9]{2}_[0-9]{2})$
    Extract metadata from:All images
    Select the filtering criteria:and (file does contain "")
    Metadata file location:Elsewhere...|
    Match file and image metadata:[]
    Use case insensitive matching?:No
    Metadata file name:
    Does cached metadata exist?:No

NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Assign a name to:All images
    Select the image type:Color image
    Name to assign these images:Original
    Match metadata:[]
    Image set matching method:Order
    Set intensity range from:Image metadata
    Assignments count:1
    Single images count:0
    Maximum intensity:255.0
    Process as 3D?:No
    Relative pixel spacing in X:1.0
    Relative pixel spacing in Y:1.0
    Relative pixel spacing in Z:1.0
    Select the rule criteria:and (file does contain "")
    Name to assign these images:DNA
    Name to assign these objects:Cell
    Select the image type:Grayscale image
    Set intensity range from:Image metadata
    Maximum intensity:255.0

Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Do you want to group your images?:No
    grouping metadata count:1
    Metadata category:Series

ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:False|notes:['seperate 3 channels to gray (gray channel needed for object recognition)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:Original
    Conversion method:Split
    Image type:Channels
    Name the output image:OrigGray
    Relative weight of the red channel:1.0
    Relative weight of the green channel:1.0
    Relative weight of the blue channel:1.0
    Convert red to gray?:Yes
    Name the output image:VWF_channel
    Convert green to gray?:No
    Name the output image:RAB_channel
    Convert blue to gray?:Yes
    Name the output image:VeCad_channel
    Convert hue to gray?:Yes
    Name the output image:OrigHue
    Convert saturation to gray?:Yes
    Name the output image:OrigSaturation
    Convert value to gray?:Yes
    Name the output image:OrigValue
    Channel count:3
    Channel number:1
    Relative weight of the channel:1.0
    Image name:Nuclei
    Channel number:2
    Relative weight of the channel:1.0
    Image name:Organelle
    Channel number:3
    Relative weight of the channel:1.0
    Image name:Cell_Membrane

Smooth:[module_num:6|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Smooth nuclei for easier object ID']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:Nuclei
    Name the output image:Smoothed_Nuclei
    Select smoothing method:Gaussian Filter
    Calculate artifact diameter automatically?:No
    Typical artifact diameter:10
    Edge intensity difference:0.3
    Clip intensities to 0 and 1?:No

Threshold:[module_num:7|svn_version:'Unknown'|variable_revision_number:12|show_window:False|notes:['increase threshold for easier Nuclei ID']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:Smoothed_Nuclei
    Name the output image:Threshold_Nuclei
    Threshold strategy:Global
    Thresholding method:Minimum Cross-Entropy
    Threshold smoothing scale:1
    Threshold correction factor:1.4
    Lower and upper bounds on threshold:0.01,1.0
    Manual threshold:0.0
    Select the measurement to threshold with:None
    Two-class or three-class thresholding?:Two classes
    Log transform before thresholding?:No
    Assign pixels in the middle intensity class to the foreground or the background?:Foreground
    Size of adaptive window:50
    Lower outlier fraction:0.05
    Upper outlier fraction:0.05
    Averaging method:Mean
    Variance method:Standard deviation
    # of deviations:2.0
    Thresholding method:Otsu

IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:['ID nuclei as objects']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:Threshold_Nuclei
    Name the primary objects to be identified:Nuclei_Object
    Typical diameter of objects, in pixel units (Min,Max):50,400
    Discard objects outside the diameter range?:Yes
    Discard objects touching the border of the image?:No
    Method to distinguish clumped objects:Shape
    Method to draw dividing lines between clumped objects:Shape
    Size of smoothing filter:10
    Suppress local maxima that are closer than this minimum allowed distance:40
    Speed up by using lower-resolution image to find local maxima?:No
    Fill holes in identified objects?:After both thresholding and declumping
    Automatically calculate size of smoothing filter for declumping?:Yes
    Automatically calculate minimum allowed distance between local maxima?:No
    Handling of objects if excessive number of objects identified:Continue
    Maximum number of objects:500
    Use advanced settings?:Yes
    Threshold setting version:12
    Threshold strategy:Global
    Thresholding method:Otsu
    Threshold smoothing scale:1
    Threshold correction factor:1.4
    Lower and upper bounds on threshold:0.0, 1.0
    Manual threshold:0.5
    Select the measurement to threshold with:None
    Two-class or three-class thresholding?:Two classes
    Log transform before thresholding?:No
    Assign pixels in the middle intensity class to the foreground or the background?:Foreground
    Size of adaptive window:10
    Lower outlier fraction:0.05
    Upper outlier fraction:0.05
    Averaging method:Mean
    Variance method:Standard deviation
    # of deviations:2
    Thresholding method:Otsu

Smooth:[module_num:9|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['smooth the cell wall for easier identification, similarly to the smoothing for the nuclei.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:Cell_Membrane
    Name the output image:CM_Smoothed
    Select smoothing method:Gaussian Filter
    Calculate artifact diameter automatically?:No
    Typical artifact diameter:5
    Edge intensity difference:0.1
    Clip intensities to 0 and 1?:Yes

IdentifySecondaryObjects:[module_num:10|svn_version:'Unknown'|variable_revision_number:10|show_window:False|notes:['Identify cells using the nuclei identified previously. through propagation, the amount of cells is equal to the amount of nuclei.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input objects:Nuclei_Object
    Name the objects to be identified:Cells_Object
    Select the method to identify the secondary objects:Propagation
    Select the input image:CM_Smoothed
    Number of pixels by which to expand the primary objects:10
    Regularization factor:0
    Discard secondary objects touching the border of the image?:Yes
    Discard the associated primary objects?:No
    Name the new primary objects:FilteredNuclei
    Fill holes in identified objects?:Yes
    Threshold setting version:12
    Threshold strategy:Global
    Thresholding method:Otsu
    Threshold smoothing scale:1
    Threshold correction factor:0
    Lower and upper bounds on threshold:0.00,1
    Manual threshold:0.0
    Select the measurement to threshold with:None
    Two-class or three-class thresholding?:Three classes
    Log transform before thresholding?:Yes
    Assign pixels in the middle intensity class to the foreground or the background?:Background
    Size of adaptive window:10
    Lower outlier fraction:0.05
    Upper outlier fraction:0.05
    Averaging method:Mean
    Variance method:Standard deviation
    # of deviations:2
    Thresholding method:Otsu

IdentifyTertiaryObjects:[module_num:11|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['create outline of cell walls.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the larger identified objects:Cells_Object
    Select the smaller identified objects:Cells_Object
    Name the tertiary objects to be identified:CM_Object
    Shrink smaller object prior to subtraction?:Yes

RescaleIntensity:[module_num:12|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['enhance VWF signal to make the image viewable after export.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:Organelle
    Name the output image:Rescale_Intensity_Organelle
    Rescaling method:Stretch each image to use the full intensity range
    Method to calculate the minimum intensity:Custom
    Method to calculate the maximum intensity:Custom
    Lower intensity limit for the input image:0.0
    Upper intensity limit for the input image:1.0
    Intensity range for the input image:0.0,1.0
    Intensity range for the output image:0.0,1.0
    Select image to match in maximum intensity:None
    Divisor value:1.0
    Divisor measurement:None

EnhanceOrSuppressFeatures:[module_num:13|svn_version:'Unknown'|variable_revision_number:7|show_window:False|notes:['Further enhancing']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:Rescale_Intensity_Organelle
    Name the output image:Speckles_Organelle
    Select the operation:Enhance
    Feature size:24
    Feature type:Speckles
    Range of hole sizes:1,10
    Smoothing scale:2.0
    Shear angle:0.0
    Decay:0.95
    Enhancement method:Tubeness
    Speed and accuracy:Fast
    Rescale result image:No

EnhanceOrSuppressFeatures:[module_num:14|svn_version:'Unknown'|variable_revision_number:7|show_window:False|notes:['further enhancing']|batch_state:array([], dtype=uint8)|enabled:False|wants_pause:False]
    Select the input image:Speckles_Organelle
    Name the output image:Neurites_Organelle
    Select the operation:Enhance
    Feature size:8
    Feature type:Neurites
    Range of hole sizes:1,10
    Smoothing scale:1.5
    Shear angle:0.0
    Decay:0.95
    Enhancement method:Line structures
    Speed and accuracy:Fast
    Rescale result image:Yes

IdentifyPrimaryObjects:[module_num:15|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:['Using the threshold to identify WPB as objects. finetuned to include long/thin WPB and short round WPB clustered together.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:Speckles_Organelle
    Name the primary objects to be identified:Organelle_Object
    Typical diameter of objects, in pixel units (Min,Max):2,25
    Discard objects outside the diameter range?:Yes
    Discard objects touching the border of the image?:Yes
    Method to distinguish clumped objects:Intensity
    Method to draw dividing lines between clumped objects:Shape
    Size of smoothing filter:0
    Suppress local maxima that are closer than this minimum allowed distance:7
    Speed up by using lower-resolution image to find local maxima?:No
    Fill holes in identified objects?:After both thresholding and declumping
    Automatically calculate size of smoothing filter for declumping?:Yes
    Automatically calculate minimum allowed distance between local maxima?:No
    Handling of objects if excessive number of objects identified:Continue
    Maximum number of objects:500
    Use advanced settings?:Yes
    Threshold setting version:12
    Threshold strategy:Adaptive
    Thresholding method:Otsu
    Threshold smoothing scale:1.6
    Threshold correction factor:0.9
    Lower and upper bounds on threshold:0.1,1.0
    Manual threshold:0.5
    Select the measurement to threshold with:None
    Two-class or three-class thresholding?:Two classes
    Log transform before thresholding?:No
    Assign pixels in the middle intensity class to the foreground or the background?:Foreground
    Size of adaptive window:12
    Lower outlier fraction:0.05
    Upper outlier fraction:0.05
    Averaging method:Mean
    Variance method:Standard deviation
    # of deviations:2.0
    Thresholding method:Otsu

MeasureObjectIntensity:[module_num:16|svn_version:'Unknown'|variable_revision_number:4|show_window:False|notes:['']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select images to measure:Organelle
    Select objects to measure:Organelle_Object

MeasureObjectSizeShape:[module_num:17|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select object sets to measure:Cells_Object, Organelle_Object
    Calculate the Zernike features?:No
    Calculate the advanced features?:No

RelateObjects:[module_num:18|svn_version:'Unknown'|variable_revision_number:5|show_window:False|notes:['relate Cells to WPBs to calculate the distance between the WPB and the cell wall and the nuclei.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Parent objects:Cells_Object
    Child objects:Organelle_Object
    Calculate child-parent distances?:Minimum
    Calculate per-parent means for all child measurements?:Yes
    Calculate distances to other parents?:Yes
    Do you want to save the children with parents as a new object set?:No
    Name the output object:None
    Parent name:Nuclei_Object

OverlayOutlines:[module_num:19|svn_version:'Unknown'|variable_revision_number:4|show_window:False|notes:['Creates a overlay of the WPB, cells, nucleus and enlarged nucleus objects on the Rab27 signal as background. this allows easy check of the pipeline for all samples.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Display outlines on a blank image?:No
    Select image on which to display outlines:Rescale_Intensity_Organelle
    Name the output image:Overlay
    Outline display mode:Color
    Select method to determine brightness of outlines:Max of image
    How to outline:Thick
    Select outline color:red
    Select objects to display:Cells_Object
    Select outline color:Green
    Select objects to display:Organelle_Object
    Select outline color:Blue
    Select objects to display:Nuclei_Object

DisplayDataOnImage:[module_num:20|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Display object or image measurements?:Object
    Select the input objects:Cells_Object
    Measurement to display:Number_Object_Number
    Select the image on which to display the measurements:Overlay
    Text color:#DE24FF
    Name the output image that has the measurements displayed:Overlay_Numbered
    Font size (points):20
    Number of decimals:0
    Image elements to save:Image
    Annotation offset (in pixels):0
    Display mode:Text
    Color map:Default
    Display background image?:Yes
    Color map scale:Use this image's measurement range
    Color map range:0.0,1.0

SaveImages:[module_num:21|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:['overlay made on rab27 is saved here. ']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
    Select the type of image to save:Image
    Select the image to save:Overlay_Numbered
    Select method for constructing file names:From image filename
    Select image name for file prefix:Original
    Enter single file name:OrigBlue
    Number of digits:4
    Append a suffix to the image file name?:No
    Text to append to the image name:
    Saved file format:png
    Output file location:Elsewhere...|
    Image bit depth:8-bit integer
    Overwrite existing files without warning?:No
    When to save:Every cycle
    Record the file and path information to the saved image?:No
    Create subfolders in the output folder?:No
    Base image folder:Elsewhere...|
    How to save the series:T (Time)
    Save with lossless compression?:Yes

ExportToDatabase:[module_num:22|svn_version:'Unknown'|variable_revision_number:28|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:True]
    Database type:SQLite
    Database name:DefaultDB
    Add a prefix to table names?:No
    Table prefix:MyExpt_
    Output file location:Elsewhere...|
    Create a CellProfiler Analyst properties file?:No
    Database host:
    Username:
    Password:
    Name the SQLite database file:xxx.db
    Calculate the per-image mean values of object measurements?:Yes
    Calculate the per-image median values of object measurements?:Yes
    Calculate the per-image standard deviation values of object measurements?:Yes
    Calculate the per-well mean values of object measurements?:No
    Calculate the per-well median values of object measurements?:No
    Calculate the per-well standard deviation values of object measurements?:No
    Export measurements for all objects to the database?:Select...
    Select the objects:Cells_Object,Organelle_Object
    Maximum # of characters in a column name:64
    Create one table per object, a single object table or a single object view?:Single object table
    Enter an image url prepend if you plan to access your files via http:
    Write image thumbnails directly to the database?:No
    Select the images for which you want to save thumbnails:
    Auto-scale thumbnail pixel intensities?:Yes
    Select the plate type:None
    Select the plate metadata:None
    Select the well metadata:None
    Include information for all images, using default values?:Yes
    Properties image group count:1
    Properties group field count:1
    Properties filter field count:0
    Workspace measurement count:1
    Experiment name:xxx
    Which objects should be used for locations?:None
    Enter a phenotype class table name if using the Classifier tool in CellProfiler Analyst:
    Export object relationships?:Yes
    Overwrite without warning?:Never
    Access CellProfiler Analyst images via URL?:No
    Select the classification type:Object
    Select an image to include:None
    Use the image name for the display?:Yes
    Image name:Channel1
    Channel color:red
    Do you want to add group fields?:No
    Enter the name of the group:
    Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well
    Do you want to add filter fields?:No
    Automatically create a filter for each plate?:No
    Create a CellProfiler Analyst workspace file?:No
    Select the measurement display tool:ScatterPlot
    Type of measurement to plot on the X-axis:Image
    Enter the object name:None
    Select the X-axis measurement:None
    Select the X-axis index:ImageNumber
    Type of measurement to plot on the Y-axis:Image
    Enter the object name:None
    Select the Y-axis measurement:None
    Select the Y-axis index:ImageNumber