CellProfiler Pipeline: http://www.cellprofiler.org Version:1 SVNRevision:11710 LoadImages:[module_num:1|svn_version:\'11587\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D] File type to be loaded:individual images File selection method:Text-Exact match Number of images in each group?:3 Type the text that the excluded images have in common:Do not use Analyze all subfolders within the selected folder?:None Input image file location:Default Input Folder\x7CNone Check image sets for missing or duplicate files?:Yes Group images by metadata?:No Exclude certain files?:No Specify metadata fields to group by: Select subfolders to analyze: Image count:1 Text that these images have in common (case-sensitive):mhh Position of this image in each group:1 Extract metadata from where?:None Regular expression that finds metadata in the file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D) Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ Channel count:1 Group the movie frames?:No Grouping method:Interleaved Number of channels per group:3 Load the input as images or objects?:Images Name this loaded image:OrigColor Name this loaded object:Nuclei Retain outlines of loaded objects?:No Name the outline image:LoadedImageOutlines Channel number:1 Rescale intensities?:Yes ColorToGray:[module_num:2|svn_version:\'10318\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split into component channels. Red seems to have the best contrast.\'\x5D] Select the input image:OrigColor Conversion method:Split Image type\x3A:RGB Name the output image:OrigGray Relative weight of the red channel:1 Relative weight of the green channel:1 Relative weight of the blue channel:1 Convert red to gray?:Yes Name the output image:OrigRed Convert green to gray?:No Name the output image:OrigGreen Convert blue to gray?:No Name the output image:OrigBlue Channel count:1 Channel number\x3A:Red\x3A 1 Relative weight of the channel:1 Image name\x3A:Channel1 ImageMath:[module_num:3|svn_version:\'10718\'|variable_revision_number:3|show_window:False|notes:\x5B\'Correction and identifcation assume light foreground on dark background so invert the pixel intensities.\'\x5D] Operation:Invert Raise the power of the result by:1 Multiply the result by:1 Add to result: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:Inverted Image or measurement?:Image Select the first image:OrigRed Multiply the first image by:1 Measurement: Image or measurement?:Image Select the second image: Multiply the second image by:1 Measurement: CorrectIlluminationCalculate:[module_num:4|svn_version:\'10458\'|variable_revision_number:2|show_window:False|notes:\x5B\'Try to correct for large-scale intensity heterogenities in the image, i.e, the fact that region arond the lymph node is lighter (darker in the original color image) than the rest. No rescaling if using background method. Adjust black size and smoothing filter size until the resulting pattern captures heterogenity but not the finer branching features.\'\x5D] Select the input image:Inverted Name the output image:Illum Select how the illumination function is calculated:Background Dilate objects in the final averaged image?:No Dilation radius:1 Block size:10 Rescale the illumination function?:No Calculate function for each image individually, or based on all images?:Each Smoothing method:Median Filter Method to calculate smoothing filter size:Manually Approximate object size:10 Smoothing filter size:50 Retain the averaged image for use later in the pipeline (for example, in SaveImages)?:No Name the averaged image:IllumBlueAvg Retain the dilated image for use later in the pipeline (for example, in SaveImages)?:No Name the dilated image:IllumBlueDilated Automatically calculate spline parameters?:Yes Background mode:auto Number of spline points:5 Background threshold:2 Image resampling factor:2 Maximum number of iterations:40 Residual value for convergence:0.001 CorrectIlluminationApply:[module_num:5|svn_version:\'10300\'|variable_revision_number:3|show_window:True|notes:\x5B\'Correct using subtraction (neccesary if using background method in module previous).\'\x5D] Select the input image:Inverted Name the output image:Corrected Select the illumination function:Illum Select how the illumination function is applied:Subtract IdentifyPrimaryObjects:[module_num:6|svn_version:\'10826\'|variable_revision_number:8|show_window:True|notes:\x5B\'Try to identify the lymph node from the illumination correction function (since it is very bright). Adjust threshold correction factor upwards accordingly.\'\x5D] Select the input image:Illum Name the primary objects to be identified:LymphNodes Typical diameter of objects, in pixel units (Min,Max):150,4000 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 Select the thresholding method:Otsu Global Threshold correction factor:1.3 Lower and upper bounds on threshold:0.000000,1.000000 Approximate fraction of image covered by objects?:0.01 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 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:PrimaryOutlines Fill holes in identified objects?:Yes Automatically calculate size of smoothing filter?:Yes Automatically calculate minimum allowed distance between local maxima?:Yes Manual threshold:0.0 Select binary image:None Retain outlines of the identified objects?:No Automatically calculate the threshold using the Otsu method?:Yes Enter Laplacian of Gaussian threshold:0.5 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?:Background Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes Enter LoG filter diameter:5 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Select the measurement to threshold with:None EnhanceOrSuppressFeatures:[module_num:7|svn_version:\'10591\'|variable_revision_number:2|show_window:True|notes:\x5B\'Enhance contrast of ducts. Feature is approx width of duct.\'\x5D] Select the input image:Corrected Name the output image:DuctFiltered Select the operation:Enhance Feature size:5 Feature type:Neurites Range of hole sizes:1,10 ExpandOrShrinkObjects:[module_num:8|svn_version:\'10830\'|variable_revision_number:1|show_window:False|notes:\x5B"I\'m going to mask out the image with the node objects; however, this leaves a bright halo enclosed the region around the node. To avoid this, expand the node by enough pixels so that no halo is apparent after masking."\x5D] Select the input objects:LymphNodes Name the output objects:ExpandedNodes Select the operation:Expand objects by a specified number of pixels Number of pixels by which to expand or shrink:10 Fill holes in objects so that all objects shrink to a single point?:No Retain the outlines of the identified objects for use later in the pipeline (for example, in SaveImages)?:No Name the outline image:ShrunkenNucleiOutlines MaskImage:[module_num:9|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\'Mask the image with the expanded node objects. This leaves just the ducts for detection.\'\x5D] Select the input image:DuctFiltered Name the output image:DuctEnhanced Use objects or an image as a mask?:Objects Select object for mask:ExpandedNodes Select image for mask:None Invert the mask?:Yes IdentifyPrimaryObjects:[module_num:10|svn_version:\'10826\'|variable_revision_number:8|show_window:True|notes:\x5B\'Identify the duct branches. Adjust the threshold correction factor as needed.\'\x5D] Select the input image:DuctEnhanced Name the primary objects to be identified:DuctSystem Typical diameter of objects, in pixel units (Min,Max):20,4000 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 Select the thresholding method:Otsu Global Threshold correction factor:1.1 Lower and upper bounds on threshold:0.000000,1.000000 Approximate fraction of image covered by objects?:0.01 Method to distinguish clumped objects:None 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 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:PrimaryOutlines Fill holes in identified objects?:No Automatically calculate size of smoothing filter?:Yes Automatically calculate minimum allowed distance between local maxima?:Yes Manual threshold:0.0 Select binary image:None Retain outlines of the identified objects?:No Automatically calculate the threshold using the Otsu method?:Yes Enter Laplacian of Gaussian threshold:0.5 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?:Background Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes Enter LoG filter diameter:5 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Select the measurement to threshold with:None EnhanceOrSuppressFeatures:[module_num:11|svn_version:\'10591\'|variable_revision_number:2|show_window:True|notes:\x5B\'Use speckle echancement on the masked image by using a filter size that is wider than the duct width but smaller than the bud diameter. This yields an image which has only the branches but not the buds.\'\x5D] Select the input image:DuctEnhanced Name the output image:BudFiltered Select the operation:Enhance Feature size:20 Feature type:Speckles Range of hole sizes:1,10 ImageMath:[module_num:12|svn_version:\'10718\'|variable_revision_number:3|show_window:True|notes:\x5B\'Subtract the bud-filtered image from the original masked image to (hopefully) get an image in which the buds are enhanced.\'\x5D] Operation:Subtract Raise the power of the result by:1 Multiply the result by:1 Add to result: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:BudEnhanced Image or measurement?:Image Select the first image:DuctEnhanced Multiply the first image by:1 Measurement: Image or measurement?:Image Select the second image:BudFiltered Multiply the second image by:1 Measurement: IdentifyPrimaryObjects:[module_num:13|svn_version:\'10826\'|variable_revision_number:8|show_window:True|notes:\x5B\'Identify the end buds. Adjust the threshold correction factor as needed.\'\x5D] Select the input image:BudEnhanced Name the primary objects to be identified:EndBuds Typical diameter of objects, in pixel units (Min,Max):20,50 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?:Yes Select the thresholding method:Otsu Global Threshold correction factor:3 Lower and upper bounds on threshold:0.000000,1.000000 Approximate fraction of image covered by objects?:0.01 Method to distinguish clumped objects:Intensity 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 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:PrimaryOutlines Fill holes in identified objects?:Yes Automatically calculate size of smoothing filter?:Yes Automatically calculate minimum allowed distance between local maxima?:Yes Manual threshold:0.0 Select binary image:None Retain outlines of the identified objects?:No Automatically calculate the threshold using the Otsu method?:Yes Enter Laplacian of Gaussian threshold:0.5 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?:Background Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes Enter LoG filter diameter:5 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:500 Select the measurement to threshold with:None MeasureImageAreaOccupied:[module_num:14|svn_version:\'10563\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure the area occupied by the ductal system.\'\x5D] Hidden:1 Measure the area occupied in a binary image, or in objects?:Objects Select objects to measure:DuctSystem Retain a binary image of the object regions?:No Name the output binary image:Stain Select a binary image to measure:None MeasureObjectSizeShape:[module_num:15|svn_version:\'1\'|variable_revision_number:1|show_window:True|notes:\x5B\'Measure the area of each of the end buds.\'\x5D] Select objects to measure:EndBuds Calculate the Zernike features?:No ExportToSpreadsheet:[module_num:16|svn_version:\'10880\'|variable_revision_number:7|show_window:True|notes:\x5B\'Export meausurements to spreadsheet. The per-image table will contain (among other things) the total area occupied by the duct system and the number of end buds. The per-bud table will contain (among other things) the area of each end bud.\'\x5D] Select or enter the column delimiter:Comma (",") Prepend the output file name to the data file names?:Yes Add image metadata columns to your object data file?:No Limit output to a size that is allowed in Excel?:No Select the columns of 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\x7CNone 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 measurements, using default file names?:No Press button to select measurements to export: Data to export:Image Combine these object measurements with those of the previous object?:No File name:DATA.csv Use the object name for the file name?:Yes Data to export:EndBuds Combine these object measurements with those of the previous object?:No File name:DATA.csv Use the object name for the file name?:Yes