CellProfiler Pipeline: http://www.cellprofiler.org Version:3 DateRevision:20151120164007 GitHash:55c1a20 ModuleCount:36 HasImagePlaneDetails:False LoadData:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Input data file location:Default Input Folder sub-folder\x7CCP_projects\\\\ilastik_CP_AssayDev\\\\GroundTruth\\\\file_lists Name of the file:File_list_full_with_GroundTruth.csv Load images based on this data?:Yes Base image location:Default Input Folder\x7C Process just a range of rows?:Yes Rows to process:1,5 Group images by metadata?:No Select metadata tags for grouping: Rescale intensities?:Yes ClassifyPixels:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'** Make sure these "classes" match your ilastik classes (0,1,2,3,... top to bottom) ** Hep=0, Fb=1, Bkgd=2\', \'There is a bug still that does not translate the path from Windows to Linux in ClassifyPixels, so we specify the path in the UNIX way here\x3A /imaging/analysis/2007_11_07_Hepatoxicity_SPARC/2013_03_27_combinatorialscreen/ilastik\', \'rather than \\\\\\\\iodine\\\\imaging_analysis\\\\2007_11_07_Hepatoxicity_SPARC\\\\2013_03_27_combinatorialscreen\\\\ilastik\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:CorrOrig Classifier file location:Elsewhere...\x7C\\\\\\\\argon\\\\imaging_analysis\\\\2007_11_07_Hepatoxicity_SPARC\\\\ilastik_CP_co-culture_METHODS_paper\\\\ilastik\\\\Seeding\\\\ilastik_classifier Classfier file name:Seeding_classifier_2015_08_28.h5 Probability map count:4 Name the output probability map:HepProb Select the class:0 Name the output probability map:FibProb Select the class:1 Name the output probability map:BkgdProb Select the class:2 Name the output probability map:DebrisProb Select the class:3 Smooth:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:HepProb Name the output image:HepSmooth Select smoothing method:Gaussian Filter Calculate artifact diameter automatically?:No Typical artifact diameter:7 Edge intensity difference:0.1 Clip intensities to 0 and 1?:Yes Smooth:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:FibProb Name the output image:FibSmooth Select smoothing method:Gaussian Filter Calculate artifact diameter automatically?:No Typical artifact diameter:7 Edge intensity difference:0.1 Clip intensities to 0 and 1?:Yes Smooth:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:DebrisProb Name the output image:DebrisSmooth Select smoothing method:Gaussian Filter Calculate artifact diameter automatically?:No Typical artifact diameter:7 Edge intensity difference:0.1 Clip intensities to 0 and 1?:Yes IdentifyPrimaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:HepSmooth Name the primary objects to be identified:HepObj Typical diameter of objects, in pixel units (Min,Max):20,60 Discard objects outside the diameter range?:Yes Try to merge too small objects with nearby larger objects?:No 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:15 Suppress local maxima that are closer than this minimum allowed distance:20 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:HepPrimaryNucOutline Fill holes in identified objects?:After both thresholding and declumping Automatically calculate size of smoothing filter for declumping?:No Automatically calculate minimum allowed distance between local maxima?:No Retain outlines of the identified objects?:Yes Automatically calculate the threshold using the Otsu method?:Yes Enter Laplacian of Gaussian threshold:0.5 Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes Enter LoG filter diameter:5.0 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Threshold setting version:1 Threshold strategy:Manual Thresholding method:Otsu Select the smoothing method for thresholding:Automatic Threshold smoothing scale:1.0 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.0,1.0 Approximate fraction of image covered by objects?:0.01 Manual threshold:0.5 Select the measurement to threshold with:None Select binary image:None Masking objects:None Two-class or three-class thresholding?:Two classes Minimize the weighted variance or the entropy?:Weighted variance Assign pixels in the middle intensity class to the foreground or the background?:Foreground Method to calculate adaptive window size:Image size Size of adaptive window:10 Use default parameters?:Default Lower outlier fraction:0.05 Upper outlier fraction:0.05 Averaging method:Mean Variance method:Standard deviation # of deviations:2.0 IdentifyPrimaryObjects:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:FibSmooth Name the primary objects to be identified:FibObj Typical diameter of objects, in pixel units (Min,Max):25,120 Discard objects outside the diameter range?:Yes Try to merge too small objects with nearby larger objects?:No 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:7.0 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:FibNucOutline 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?:Yes Retain outlines of the identified objects?:Yes Automatically calculate the threshold using the Otsu method?:Yes Enter Laplacian of Gaussian threshold:0.5 Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes Enter LoG filter diameter:5.0 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Threshold setting version:1 Threshold strategy:Manual Thresholding method:Otsu Select the smoothing method for thresholding:Automatic Threshold smoothing scale:1.0 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.0,1.0 Approximate fraction of image covered by objects?:0.01 Manual threshold:0.5 Select the measurement to threshold with:None Select binary image:None Masking objects:None Two-class or three-class thresholding?:Two classes Minimize the weighted variance or the entropy?:Weighted variance Assign pixels in the middle intensity class to the foreground or the background?:Foreground Method to calculate adaptive window size:Image size Size of adaptive window:10 Use default parameters?:Default Lower outlier fraction:0.05 Upper outlier fraction:0.05 Averaging method:Mean Variance method:Standard deviation # of deviations:2.0 IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:DebrisSmooth Name the primary objects to be identified:DebrisObj Typical diameter of objects, in pixel units (Min,Max):25,120 Discard objects outside the diameter range?:Yes Try to merge too small objects with nearby larger objects?:No 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:7.0 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:DebrisOutline 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?:Yes Retain outlines of the identified objects?:Yes Automatically calculate the threshold using the Otsu method?:Yes Enter Laplacian of Gaussian threshold:0.5 Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes Enter LoG filter diameter:5.0 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Threshold setting version:1 Threshold strategy:Manual Thresholding method:Otsu Select the smoothing method for thresholding:Automatic Threshold smoothing scale:1.0 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.0,1.0 Approximate fraction of image covered by objects?:0.01 Manual threshold:0.5 Select the measurement to threshold with:None Select binary image:None Masking objects:None Two-class or three-class thresholding?:Two classes Minimize the weighted variance or the entropy?:Weighted variance Assign pixels in the middle intensity class to the foreground or the background?:Foreground Method to calculate adaptive window size:Image size Size of adaptive window:10 Use default parameters?:Default Lower outlier fraction:0.05 Upper outlier fraction:0.05 Averaging method:Mean Variance method:Standard deviation # of deviations:2.0 MeasureObjectIntensity:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Hidden:1 Select an image to measure:CorrOrig Select objects to measure:FibObj Select objects to measure:HepObj MeasureObjectSizeShape:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select objects to measure:FibObj Select objects to measure:HepObj Calculate the Zernike features?:No RelateObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Sanity check. Hep and Fib should not overlap.\', \'Should really add Debris, but they are rare enough to neglect.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input child objects:HepObj Select the input parent objects:FibObj Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None FlagImage:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Sanity check. Hep and Fib should not overlap (probably, depending on smoothing before segmentation).\', \'Fibroblast objects, set aritrarily as "Parents", should have no Children fibroblast objects, i.e. the identified objects should not overlap.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Hidden:1 Hidden:1 Name the flag\'s category:Metadata Name the flag:OverlapFlag Flag if any, or all, measurement(s) fails to meet the criteria?:Flag if any fail Skip image set if flagged?:No Flag is based on:Measurements for all objects in each image Select the object to be used for flagging:FibObj Which measurement?:Children_HepObj_Count Flag images based on low values?:No Minimum value:0.0 Flag images based on high values?:Yes Maximum value:0 Rules file location:Elsewhere...\x7C Rules file name:rules.txt Class number: OverlayOutlines:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Display outlines on a blank image?:No Select image on which to display outlines:CorrOrig Name the output image:HepFibOverlay Outline display mode:Color Select method to determine brightness of outlines:Max of image Width of outlines:1 Select outlines to display:HepPrimaryNucOutline Select outline color:green Load outlines from an image or objects?:Image Select objects to display:FibGrdTrthLabelMatrix Select outlines to display:FibNucOutline Select outline color:red Load outlines from an image or objects?:Image Select objects to display:FibObj SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:False|wants_pause:False] Select the type of image to save:Image Select the image to save:HepFibOverlay Select the objects to save:None Select the module display window to save:None Select method for constructing file names:From image filename Select image name for file prefix:CorrOrig Enter single file name:OrigBlue Number of digits:4 Append a suffix to the image file name?:Yes Text to append to the image name:_HepFibOverlay Saved file format:tif Output file location:Default Output Folder sub-folder\x7COverlays Image bit depth:8-bit integer Overwrite existing files without warning?:Yes When to save:Every cycle Rescale the images? :No Save as grayscale or color image?:Grayscale Select colormap:Default Record the file and path information to the saved image?:Yes Create subfolders in the output folder?:No Base image folder:Elsewhere...\x7C Saved movie format:avi ColorToGray:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:GroundTruth Conversion method:Split Image type:RGB 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:OrigRedGTHep Convert green to gray?:Yes Name the output image:OrigGreenGTFib Convert blue to gray?:Yes Name the output image:OrigBlueGTDebris 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:1 Channel number:Red\x3A 1 Relative weight of the channel:1.0 Image name:Channel1 ImageMath:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Operation:Maximum Raise the power of the result by:1.0 Multiply the result by:1.0 Add to result:0.0 Set values less than 0 equal to 0?:Yes Set values greater than 1 equal to 1?:Yes Ignore the image masks?:No Name the output image:ImageMax Image or measurement?:Image Select the first image:OrigBlueGTDebris Multiply the first image by:1.0 Measurement: Image or measurement?:Image Select the second image:OrigGreenGTFib Multiply the second image by:1.0 Measurement: Image or measurement?:Image Select the third image:OrigRedGTHep Multiply the third image by:1.0 Measurement: IdentifyPrimaryObjects:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:OrigBlueGTDebris Name the primary objects to be identified:DebrisGTpoints Typical diameter of objects, in pixel units (Min,Max):3,4 Discard objects outside the diameter range?:Yes Try to merge too small objects with nearby larger objects?:No Discard objects touching the border of the image?:No Method to distinguish clumped objects:None Method to draw dividing lines between clumped objects:Intensity Size of smoothing filter:10 Suppress local maxima that are closer than this minimum allowed distance:7.0 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:PrimaryOutlines Fill holes in identified objects?:Never Automatically calculate size of smoothing filter for declumping?:Yes Automatically calculate minimum allowed distance between local maxima?:Yes Retain outlines of the identified objects?:No Automatically calculate the threshold using the Otsu method?:Yes Enter Laplacian of Gaussian threshold:0.5 Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes Enter LoG filter diameter:5.0 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Threshold setting version:1 Threshold strategy:Manual Thresholding method:Otsu Select the smoothing method for thresholding:Automatic Threshold smoothing scale:1.0 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.0,1.0 Approximate fraction of image covered by objects?:0.01 Manual threshold:0.999 Select the measurement to threshold with:None Select binary image:None Masking objects:None Two-class or three-class thresholding?:Two classes Minimize the weighted variance or the entropy?:Weighted variance Assign pixels in the middle intensity class to the foreground or the background?:Foreground Method to calculate adaptive window size:Image size Size of adaptive window:10 Use default parameters?:Default Lower outlier fraction:0.05 Upper outlier fraction:0.05 Averaging method:Mean Variance method:Standard deviation # of deviations:2.0 IdentifyPrimaryObjects:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:OrigGreenGTFib Name the primary objects to be identified:FibGTpoints Typical diameter of objects, in pixel units (Min,Max):3,4 Discard objects outside the diameter range?:Yes Try to merge too small objects with nearby larger objects?:No Discard objects touching the border of the image?:No Method to distinguish clumped objects:None Method to draw dividing lines between clumped objects:Intensity Size of smoothing filter:10 Suppress local maxima that are closer than this minimum allowed distance:7.0 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:PrimaryOutlines Fill holes in identified objects?:Never Automatically calculate size of smoothing filter for declumping?:Yes Automatically calculate minimum allowed distance between local maxima?:Yes Retain outlines of the identified objects?:No Automatically calculate the threshold using the Otsu method?:Yes Enter Laplacian of Gaussian threshold:0.5 Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes Enter LoG filter diameter:5.0 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Threshold setting version:1 Threshold strategy:Manual Thresholding method:Otsu Select the smoothing method for thresholding:Automatic Threshold smoothing scale:1.0 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.0,1.0 Approximate fraction of image covered by objects?:0.01 Manual threshold:0.999 Select the measurement to threshold with:None Select binary image:None Masking objects:None Two-class or three-class thresholding?:Two classes Minimize the weighted variance or the entropy?:Weighted variance Assign pixels in the middle intensity class to the foreground or the background?:Foreground Method to calculate adaptive window size:Image size Size of adaptive window:10 Use default parameters?:Default Lower outlier fraction:0.05 Upper outlier fraction:0.05 Averaging method:Mean Variance method:Standard deviation # of deviations:2.0 IdentifyPrimaryObjects:[module_num:19|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:OrigRedGTHep Name the primary objects to be identified:HepGTpoints Typical diameter of objects, in pixel units (Min,Max):3,4 Discard objects outside the diameter range?:Yes Try to merge too small objects with nearby larger objects?:No Discard objects touching the border of the image?:No Method to distinguish clumped objects:None Method to draw dividing lines between clumped objects:Intensity Size of smoothing filter:10 Suppress local maxima that are closer than this minimum allowed distance:7.0 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:PrimaryOutlines Fill holes in identified objects?:Never Automatically calculate size of smoothing filter for declumping?:Yes Automatically calculate minimum allowed distance between local maxima?:Yes Retain outlines of the identified objects?:No Automatically calculate the threshold using the Otsu method?:Yes Enter Laplacian of Gaussian threshold:0.5 Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes Enter LoG filter diameter:5.0 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Threshold setting version:1 Threshold strategy:Manual Thresholding method:Otsu Select the smoothing method for thresholding:Automatic Threshold smoothing scale:1.0 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.0,1.0 Approximate fraction of image covered by objects?:0.01 Manual threshold:0.999 Select the measurement to threshold with:None Select binary image:None Masking objects:None Two-class or three-class thresholding?:Two classes Minimize the weighted variance or the entropy?:Weighted variance Assign pixels in the middle intensity class to the foreground or the background?:Foreground Method to calculate adaptive window size:Image size Size of adaptive window:10 Use default parameters?:Default Lower outlier fraction:0.05 Upper outlier fraction:0.05 Averaging method:Mean Variance method:Standard deviation # of deviations:2.0 IdentifyPrimaryObjects:[module_num:20|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Construct this "All" Ground Truth object to allow for a combined IDSecondary such that "grown out" borders don\\\'t overlap between object types.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:ImageMax Name the primary objects to be identified:AllGTpoints Typical diameter of objects, in pixel units (Min,Max):3,4 Discard objects outside the diameter range?:Yes Try to merge too small objects with nearby larger objects?:No Discard objects touching the border of the image?:No Method to distinguish clumped objects:None Method to draw dividing lines between clumped objects:Intensity Size of smoothing filter:10 Suppress local maxima that are closer than this minimum allowed distance:7.0 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:PrimaryOutlines Fill holes in identified objects?:Never Automatically calculate size of smoothing filter for declumping?:Yes Automatically calculate minimum allowed distance between local maxima?:Yes Retain outlines of the identified objects?:No Automatically calculate the threshold using the Otsu method?:Yes Enter Laplacian of Gaussian threshold:0.5 Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes Enter LoG filter diameter:5.0 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Threshold setting version:1 Threshold strategy:Manual Thresholding method:Otsu Select the smoothing method for thresholding:Automatic Threshold smoothing scale:1.0 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.0,1.0 Approximate fraction of image covered by objects?:0.01 Manual threshold:0.999 Select the measurement to threshold with:None Select binary image:None Masking objects:None Two-class or three-class thresholding?:Two classes Minimize the weighted variance or the entropy?:Weighted variance Assign pixels in the middle intensity class to the foreground or the background?:Foreground Method to calculate adaptive window size:Image size Size of adaptive window:10 Use default parameters?:Default Lower outlier fraction:0.05 Upper outlier fraction:0.05 Averaging method:Mean Variance method:Standard deviation # of deviations:2.0 RelateObjects:[module_num:21|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'True Pos - Hep\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input child objects:HepObj Select the input parent objects:HepGTpoints Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None RelateObjects:[module_num:22|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'False Pos Fib - True Hep\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input child objects:FibObj Select the input parent objects:HepGTpoints Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None RelateObjects:[module_num:23|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'False Pos Debris - True Hep\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input child objects:DebrisObj Select the input parent objects:HepGTpoints Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None RelateObjects:[module_num:24|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'True Pos - Fib\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input child objects:FibObj Select the input parent objects:FibGTpoints Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None RelateObjects:[module_num:25|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'False Pos Hep- True Fib\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input child objects:HepObj Select the input parent objects:FibGTpoints Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None RelateObjects:[module_num:26|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'False Pos Debris - True Fib\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input child objects:DebrisObj Select the input parent objects:FibGTpoints Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None RelateObjects:[module_num:27|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'True Pos Debris\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input child objects:DebrisObj Select the input parent objects:DebrisGTpoints Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None RelateObjects:[module_num:28|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'False Pos Hep - True Debris\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input child objects:HepObj Select the input parent objects:DebrisGTpoints Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None RelateObjects:[module_num:29|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'False Pos Fib - True Debris\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input child objects:FibObj Select the input parent objects:DebrisGTpoints Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None IdentifySecondaryObjects:[module_num:30|svn_version:\'Unknown\'|variable_revision_number:9|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:False|wants_pause:False] Select the input objects:AllGTpoints Name the objects to be identified:AllGTWholeCells Select the method to identify the secondary objects:Distance - B Select the input image:ImageMax Number of pixels by which to expand the primary objects:50 Regularization factor:0.05 Name the outline image:SecondaryOutlines Retain outlines of the identified secondary objects?:No Discard secondary objects touching the border of the image?:No Discard the associated primary objects?:No Name the new primary objects:FilteredNuclei Retain outlines of the new primary objects?:No Name the new primary object outlines:FilteredNucleiOutlines Fill holes in identified objects?:Yes Threshold setting version:1 Threshold strategy:Global Thresholding method:Otsu Select the smoothing method for thresholding:No smoothing Threshold smoothing scale:1.0 Threshold correction factor:3 Lower and upper bounds on threshold:0.0,1.0 Approximate fraction of image covered by objects?:0.01 Manual threshold:0.0 Select the measurement to threshold with:None Select binary image:None Masking objects:None Two-class or three-class thresholding?:Three classes Minimize the weighted variance or the entropy?:Weighted variance Assign pixels in the middle intensity class to the foreground or the background?:Foreground Method to calculate adaptive window size:Image size Size of adaptive window:10 Use default parameters?:Default Lower outlier fraction:0.05 Upper outlier fraction:0.05 Averaging method:Mean Variance method:Standard deviation # of deviations:2.0 RelateObjects:[module_num:31|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:False|wants_pause:False] Select the input child objects:HepGTpoints Select the input parent objects:AllGTWholeCells Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None FilterObjects:[module_num:32|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:False|wants_pause:False] Name the output objects:HepGTwholeCells Select the object to filter:AllGTWholeCells Select the filtering mode:Measurements Select the filtering method:Limits Select the objects that contain the filtered objects:None Retain outlines of the identified objects?:No Name the outline image:FilteredObjects Rules file location:Elsewhere...\x7C Rules file name:rules.txt Class number:1 Measurement count:1 Additional object count:0 Assign overlapping child to:Both parents Select the measurement to filter by:Children_HepGTpoints_Count Filter using a minimum measurement value?:Yes Minimum value:1 Filter using a maximum measurement value?:No Maximum value:1.0 CalculateImageOverlap:[module_num:33|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:False|wants_pause:False] Compare segmented objects, or foreground/background?:Segmented objects Select the image to be used as the ground truth basis for calculating the amount of overlap:None Select the image to be used to test for overlap:None Select the objects to be used as the ground truth basis for calculating the amount of overlap:HepGTwholeCells Select the objects to be tested for overlap against the ground truth:HepObj Calculate earth mover\'s distance?:No Maximum # of points:250 Point selection method:K Means Maximum distance:250 Penalize missing pixels:No ExportToSpreadsheet:[module_num:34|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True] Select the column delimiter:Comma (",") Add image metadata columns to your object data file?:No Limit output to a size that is allowed in Excel?:No Select the measurements to export:No Calculate the per-image mean values for object measurements?:No Calculate the per-image median values for object measurements?:No Calculate the per-image standard deviation values for object measurements?:No Output file location:Default Output Folder\x7C Create a GenePattern GCT file?:No Select source of sample row name:Metadata Select the image to use as the identifier:None Select the metadata to use as the identifier:None Export all measurement types?:Yes : Representation of Nan/Inf:NaN Add a prefix to file names?:Yes Filename prefix:GT_ilastik_counts_ Overwrite existing files without warning?:No Data to export:Do not use Combine these object measurements with those of the previous object?:No File name:DATA.csv Use the object name for the file name?:Yes ExportToDatabase:[module_num:35|svn_version:\'Unknown\'|variable_revision_number:26|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:False|wants_pause:True] Database type:MySQL Database name:2007_11_07_Hepatotoxicity_1 Add a prefix to table names?:Yes Table prefix:AssayDev_ilastik_Plate_Seeding SQL file prefix:SQL_ Output file location:Default Output Folder\x7C Create a CellProfiler Analyst properties file?:Yes Database host:imgdb02 Username:cpuser Password:cPus3r Name the SQLite database file:DefaultDB.db Calculate the per-image mean values of object measurements?:Yes Calculate the per-image median values of object measurements?:No Calculate the per-image standard deviation values of object measurements?:No 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?:All Select the objects: Maximum # of characters in a column name:64 Create one table per object, a single object table or a single object view?:One table per object type Enter an image url prepend if you plan to access your files via http:http\x3A//imageweb/images/CPALinks Write image thumbnails directly to the database?:Yes Select the images for which you want to save thumbnails:CorrOrig Auto-scale thumbnail pixel intensities?:Yes Select the plate type:384 Select the plate metadata:Plate Select the well metadata:Well Include information for all images, using default values?:No Properties image group count:2 Properties group field count:2 Properties filter field count:0 Workspace measurement count:1 Experiment name:MyExpt Which objects should be used for locations?:HepObj Enter a phenotype class table name if using the classifier tool: Export object relationships?:Yes Overwrite without warning?:Data and schema Access CPA images via URL?:Yes Select an image to include:CorrOrig Use the image name for the display?:Yes Image name:Channel1 Channel color:gray Select an image to include:HepFibOverlay Use the image name for the display?:Yes Image name:Channel2 Channel color:green Do you want to add group fields?:Yes Enter the name of the group:PlateWell Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well Enter the name of the group:PlateName Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate 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 CreateBatchFiles:[module_num:36|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:False|wants_pause:False] Store batch files in default output folder?:Yes Output folder path:\\\\\\\\iodine\\\\imaging_analysis\\\\2007_11_07_Hepatoxicity_SPARC\\\\2013_03_27_combinatorialscreen\\\\Main_pipeline_output\\\\2014_01_27_CP2p1_TEST Are the cluster computers running Windows?:No Hidden\x3A in batch mode:No Hidden\x3A in distributed mode:No Hidden\x3A default input folder at time of save:\\\\\\\\iodine\\\\cbnt_cbimageX\\\\HCS\\\\shanmeghan Hidden\x3A revision number:0 Hidden\x3A from old matlab:No Launch BatchProfiler:Yes Local root path:\\\\\\\\iodine\\\\cbnt_cbimageX\\\\ Cluster root path:/cbnt/cbimageX/ Local root path:\\\\\\\\argon\\\\imaging_analysis\\\\ Cluster root path:/imaging/analysis/