***** To use in CellProfiler delete the top four lines in this file and change file extension from ".txt" to ".cppipe" ***** CellProfiler Pipeline: http://www.cellprofiler.org Version:3 DateRevision:20140723173957 GitHash:6c2d896 ModuleCount:22 HasImagePlaneDetails:False Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'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.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] : Filter images?:No filtering Select the rule criteria:and (extension does isimage) (directory doesnot startwith ".") Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'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.\'\x5D|batch_state:array(\x5B\x5D, 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 image file headers Metadata source:File name Regular expression:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ Extract metadata from:All images Select the filtering criteria:and (file does contain "") Metadata file location: Match file and image metadata:\x5B\x5D Use case insensitive matching?:No NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:5|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Load the images by matching files in the folder against the unique text pattern for each stain\x3A d0.tif for nuclei, d1.tif for the PH3 image, d2.tif for the cell stain image. The three images together comprise an image set.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Assign a name to:Images matching rules Select the image type:Grayscale image Name to assign these images:DNA Match metadata:\x5B\x5D Image set matching method:Order Set intensity range from:Image metadata Assignments count:4 Single images count:0 Select the rule criteria:and (file does contain "DAPI") Name to assign these images:OrigBlue Name to assign these objects:Cell Select the image type:Grayscale image Set intensity range from:Image metadata Retain outlines of loaded objects?:No Name the outline image:LoadedObjects Select the rule criteria:and (file does contain "FITC") Name to assign these images:OrigGreen Name to assign these objects:Nucleus Select the image type:Grayscale image Set intensity range from:Image metadata Retain outlines of loaded objects?:No Name the outline image:LoadedObjects Select the rule criteria:and (file does contain "TRITC") Name to assign these images:OrigRed Name to assign these objects:Cytoplasm Select the image type:Grayscale image Set intensity range from:Image metadata Retain outlines of loaded objects?:No Name the outline image:LoadedObjects Select the rule criteria:and (file does contain "Cy5") Name to assign these images:OrigFarRed Name to assign these objects:Speckle Select the image type:Grayscale image Set intensity range from:Image metadata Retain outlines of loaded objects?:No Name the outline image:LoadedOutlines Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'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.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Do you want to group your images?:No grouping metadata count:1 Metadata category:None ApplyThreshold:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:OrigBlue Name the output image:ThreshBlue Select the output image type:Grayscale Set pixels below or above the threshold to zero?:Below threshold Subtract the threshold value from the remaining pixel intensities?:Yes Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 Threshold setting version:1 Threshold strategy:Adaptive Thresholding method:Otsu Select the smoothing method for thresholding:No smoothing Threshold smoothing scale:1.0 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.01,1.0 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 IdentifyPrimaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify the nuclei from the nuclear stain image. Some manual adjustment of the smoothing filter size and maxima supression distance is required to optimize segmentation.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:ThreshBlue Name the primary objects to be identified:Nuclei Typical diameter of objects, in pixel units (Min,Max):20,150 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 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:5 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:NucOutlines 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:.5 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 Threshold setting version:1 Threshold strategy:Adaptive Thresholding method:Otsu Select the smoothing method for thresholding:Automatic Threshold smoothing scale:1 Threshold correction factor:1 Lower and upper bounds on threshold:0.01,1 Approximate fraction of image covered by objects?:0.1 Manual threshold:0.0 Select the measurement to threshold with:None Select binary image:MoG Global Masking objects:From image 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 Method to calculate adaptive window size:Image size Size of adaptive window:10 FlagImage:[module_num:7|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] Hidden:1 Hidden:1 Name the flag\'s category:Metadata Name the flag:NoCell Flag if any, or all, measurement(s) fails to meet the criteria?:Flag if any fail Skip image set if flagged?:Yes Flag is based on:Measurements for all objects in each image Select the object to be used for flagging:Nuclei Which measurement?:Number_Object_Number Flag images based on low values?:Yes Minimum value:1 Flag images based on high values?:No Maximum value:1.0 Rules file location:Elsewhere...\x7C Rules file name:rules.txt Class number: ApplyThreshold:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:OrigGreen Name the output image:ThreshGreen Select the output image type:Grayscale Set pixels below or above the threshold to zero?:Below threshold Subtract the threshold value from the remaining pixel intensities?:Yes Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 Threshold setting version:1 Threshold strategy:Adaptive Thresholding method:RobustBackground Select the smoothing method for thresholding:No smoothing 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:0.0 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?:Background Method to calculate adaptive window size:Image size Size of adaptive window:10 ApplyThreshold:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:OrigRed Name the output image:ThreshRed Select the output image type:Grayscale Set pixels below or above the threshold to zero?:Below threshold Subtract the threshold value from the remaining pixel intensities?:Yes Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 Threshold setting version:1 Threshold strategy:Adaptive Thresholding method:Otsu Select the smoothing method for thresholding:No smoothing 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:0.0 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 ApplyThreshold:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:OrigFarRed Name the output image:ThreshFarRed Select the output image type:Grayscale Set pixels below or above the threshold to zero?:Below threshold Subtract the threshold value from the remaining pixel intensities?:Yes Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 Threshold setting version:1 Threshold strategy:Adaptive Thresholding method:Otsu Select the smoothing method for thresholding:No smoothing 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:0.0 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 IdentifyPrimaryObjects:[module_num:11|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:ThreshGreen Name the primary objects to be identified:EdU Typical diameter of objects, in pixel units (Min,Max):20,150 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 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:5 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:NucOutlines 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:.5 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 Threshold setting version:1 Threshold strategy:Adaptive Thresholding method:Otsu Select the smoothing method for thresholding:Automatic Threshold smoothing scale:1 Threshold correction factor:1 Lower and upper bounds on threshold:0.02,1 Approximate fraction of image covered by objects?:0.1 Manual threshold:0.0 Select the measurement to threshold with:None Select binary image:MoG Global Masking objects:From image 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 Method to calculate adaptive window size:Image size Size of adaptive window:10 IdentifySecondaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:9|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input objects:Nuclei Name the objects to be identified:HER2_cells Select the method to identify the secondary objects:Watershed - Image Select the input image:OrigFarRed Number of pixels by which to expand the primary objects:10 Regularization factor:0 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:Adaptive Thresholding method:Otsu Select the smoothing method for thresholding:No smoothing Threshold smoothing scale:1.0 Threshold correction factor:1.0 Lower and upper bounds on threshold:0.01,1.0 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 RelateObjects:[module_num:13|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 child objects:EdU Select the input parent objects:Nuclei 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:14|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Name the output objects:FilteredEdU Select the object to filter:Nuclei 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_EdU_Count Filter using a minimum measurement value?:Yes Minimum value:1 Filter using a maximum measurement value?:No Maximum value:1.0 CalculateMath:[module_num:15|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] Name the output measurement:EdUPositive Operation:Divide Select the numerator measurement type:Image Select the numerator objects:None Select the numerator measurement:Count_FilteredEdU Multiply the above operand by:1.0 Raise the power of above operand by:1.0 Select the denominator measurement type:Image Select the denominator objects:None Select the denominator measurement:Count_Nuclei Multiply the above operand by:1.0 Raise the power of above operand by:1.0 Take log10 of result?:No Multiply the result by:100 Raise the power of result by:1.0 Add to the result:0.0 Constrain the result to a lower bound?:No Enter the lower bound:0.0 Constrain the result to an upper bound?:No Enter the upper bound:1.0 MeasureObjectIntensity:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure intensity features from nuclei, cell and cytoplasm objects against the cropped images.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Hidden:8 Select an image to measure:OrigBlue Select an image to measure:OrigGreen Select an image to measure:OrigRed Select an image to measure:OrigFarRed Select an image to measure:ThreshBlue Select an image to measure:ThreshGreen Select an image to measure:ThreshRed Select an image to measure:ThreshFarRed Select objects to measure:Nuclei Select objects to measure:HER2_cells MeasureObjectSizeShape:[module_num:17|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:Nuclei Select objects to measure:HER2_cells Calculate the Zernike features?:No MeasureObjectNeighbors:[module_num:18|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 objects to measure:HER2_cells Select neighboring objects to measure:HER2_cells Method to determine neighbors:Adjacent Neighbor distance:5 Retain the image of objects colored by numbers of neighbors?:No Name the output image:ObjectNeighborCount Select colormap:Default Retain the image of objects colored by percent of touching pixels?:No Name the output image:PercentTouching Select a colormap:Default OverlayOutlines:[module_num:19|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:OrigFarRed Name the output image:Overlay Outline display mode:Color Select method to determine brightness of outlines:Max of image Width of outlines:1 Select outlines to display:None Select outline color:#FF8000 Load outlines from an image or objects?:Objects Select objects to display:HER2_cells Select outlines to display:None Select outline color:blue Load outlines from an image or objects?:Objects Select objects to display:Nuclei DisplayDataOnImage:[module_num:20|svn_version:\'Unknown\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Display object or image measurements?:Object Select the input objects:Nuclei Measurement to display:Number_Object_Number Select the image on which to display the measurements:Overlay Text color:red Name the output image that has the measurements displayed:DisplayImage Font size (points):8 Number of decimals:0 Image elements to save:Image Annotation offset (in pixels):0 Display mode:Text Color map:Default Display background image?:Yes SaveImages:[module_num:21|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the type of image to save:Image Select the image to save:DisplayImage Select the objects to save:None Select the module display window to save:None Select method for constructing file names:Sequential numbers Select image name for file prefix:None Enter file prefix:OverLay Number of digits:1 Append a suffix to the image file name?:No Text to append to the image name: Saved file format:png Output file location:Default Input Folder sub-folder\x7CDesktop\\\\\\\\11112015_2500Pa_Lap_Rep1_Images Image bit depth:8 Overwrite existing files without warning?:Yes When to save:Every cycle Rescale the images? :No Save as grayscale or color image?:Grayscale Select colormap:gray Record the file and path information to the saved image?:No Create subfolders in the output folder?:No Base image folder:Elsewhere...\x7C Saved movie format:avi ExportToSpreadsheet:[module_num:22|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B"Export any measurements to a comma-delimited file (.csv). The measurements made for the nuclei, cell and cytoplasm objects will be saved to separate .csv files, in addition to the per-image .csv\'s."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 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:Yes 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 Input Folder sub-folder\x7CDesktop\\\\\\\\11112015_2500Pa_Lap_Rep1_CSV 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?:No Press button to select measurements to export:Image\x7CCount_Nuclei,Image\x7CCount_FilteredEdU,Image\x7CCount_EdU,Image\x7CFileName_OrigBlue,Image\x7CPathName_OrigBlue,Image\x7CMath_EdUPositive,HER2_cells\x7CNeighbors_AngleBetweenNeighbors_Adjacent,HER2_cells\x7CNeighbors_FirstClosestDistance_Adjacent,HER2_cells\x7CNeighbors_FirstClosestObjectNumber_Adjacent,HER2_cells\x7CNeighbors_SecondClosestObjectNumber_Adjacent,HER2_cells\x7CNeighbors_PercentTouching_Adjacent,HER2_cells\x7CNeighbors_NumberOfNeighbors_Adjacent,HER2_cells\x7CNeighbors_SecondClosestDistance_Adjacent,HER2_cells\x7CIntensity_MeanIntensity_ThreshGreen,HER2_cells\x7CIntensity_MeanIntensity_ThreshBlue,HER2_cells\x7CIntensity_MeanIntensity_OrigRed,HER2_cells\x7CIntensity_MeanIntensity_OrigGreen,HER2_cells\x7CIntensity_MeanIntensity_ThreshFarRed,HER2_cells\x7CIntensity_MeanIntensity_OrigBlue,HER2_cells\x7CIntensity_MeanIntensity_ThreshRed,HER2_cells\x7CIntensity_MeanIntensity_OrigFarRed,HER2_cells\x7CIntensity_MedianIntensity_ThreshBlue,HER2_cells\x7CIntensity_MedianIntensity_ThreshGreen,HER2_cells\x7CIntensity_MedianIntensity_OrigRed,HER2_cells\x7CIntensity_MedianIntensity_OrigGreen,HER2_cells\x7CIntensity_MedianIntensity_ThreshFarRed,HER2_cells\x7CIntensity_MedianIntensity_OrigBlue,HER2_cells\x7CIntensity_MedianIntensity_ThreshRed,HER2_cells\x7CIntensity_MedianIntensity_OrigFarRed,HER2_cells\x7CAreaShape_FormFactor,HER2_cells\x7CAreaShape_MajorAxisLength,HER2_cells\x7CAreaShape_Solidity,HER2_cells\x7CAreaShape_Eccentricity,HER2_cells\x7CAreaShape_Compactness,HER2_cells\x7CAreaShape_Extent,HER2_cells\x7CAreaShape_Area,Nuclei\x7CIntensity_MedianIntensity_ThreshGreen,Nuclei\x7CIntensity_MedianIntensity_ThreshBlue,Nuclei\x7CIntensity_MedianIntensity_OrigRed,Nuclei\x7CIntensity_MedianIntensity_OrigGreen,Nuclei\x7CIntensity_MedianIntensity_ThreshFarRed,Nuclei\x7CIntensity_MedianIntensity_OrigBlue,Nuclei\x7CIntensity_MedianIntensity_ThreshRed,Nuclei\x7CIntensity_MedianIntensity_OrigFarRed,Nuclei\x7CIntensity_MeanIntensity_ThreshGreen,Nuclei\x7CIntensity_MeanIntensity_ThreshBlue,Nuclei\x7CIntensity_MeanIntensity_OrigRed,Nuclei\x7CIntensity_MeanIntensity_OrigGreen,Nuclei\x7CIntensity_MeanIntensity_ThreshFarRed,Nuclei\x7CIntensity_MeanIntensity_OrigBlue,Nuclei\x7CIntensity_MeanIntensity_ThreshRed,Nuclei\x7CIntensity_MeanIntensity_OrigFarRed,Nuclei\x7CAreaShape_FormFactor,Nuclei\x7CAreaShape_Solidity,Nuclei\x7CAreaShape_Area,Nuclei\x7CAreaShape_MajorAxisLength,Nuclei\x7CAreaShape_Compactness,Nuclei\x7CAreaShape_Extent,Nuclei\x7CAreaShape_Eccentricity Representation of Nan/Inf:NaN Add a prefix to file names?:No Filename prefix\x3A:10192015_ Overwrite without warning?:Yes Data to export:Image Combine these object measurements with those of the previous object?:No File name:11112015_2500Pa_Lap_Rep1_Image.csv Use the object name for the file name?:No Data to export:Nuclei Combine these object measurements with those of the previous object?:Yes File name:11112015_2500Pa_Lap_Rep1_Detail.csv Use the object name for the file name?:No Data to export:HER2_cells Combine these object measurements with those of the previous object?:Yes File name:DATA.csv Use the object name for the file name?:Yes Data to export:Object relationships Combine these object measurements with those of the previous object?:No File name:11112015_2500Pa_Lap_Rep1_Objectrelationship.csv Use the object name for the file name?:No