CellProfiler 4 pipeline updates

CellProfiler3Pipelines/ExampleNeighbors.cppipe

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+CellProfiler Pipeline: http://www.cellprofiler.org
+Version:3
+DateRevision:300
+GitHash:
+ModuleCount:15
+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?:Images only
+    Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.")
+
+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?:No
+    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>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)_w(?P<ChannelNumber>\x5B0-9\x5D)
+    Regular expression to extract from folder name:(?P<Date>\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:8|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.\', \'\\xe2\\x80\\x94\', \'Load the color image by matching files in the folder against the text \\xe2\\x80\\x98.JPG\\xe2\\x80\\x99\'\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: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 "1.JPG")
+    Name to assign these images:Original
+    Name to assign these objects:Cell
+    Select the image type:Color 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:\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
+
+Crop:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Crop the color image to exclude the text labels by entering specific cropping coordinates.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input image:Original
+    Name the output image:Cropped
+    Select the cropping shape:Rectangle
+    Select the cropping method:Coordinates
+    Apply which cycle\'s cropping pattern?:First
+    Left and right rectangle positions:45,629
+    Top and bottom rectangle positions:1,445
+    Coordinates of ellipse center:500,500
+    Ellipse radius, X direction:400
+    Ellipse radius, Y direction:200
+    Remove empty rows and columns?:No
+    Select the masking image:None
+    Select the image with a cropping mask:None
+    Select the objects:None
+
+ColorToGray:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Retain the red channel for later segmentation.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
+    Select the input image:Cropped
+    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:OrigRed
+    Convert green to gray?:No
+    Name the output image:OrigGreen
+    Convert blue to gray?:No
+    Name the output image:OrigBlue
+    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:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Since object identification assumes light objects against a dark background, invert the grayscale intensities of the red channel so the dark areas appear bright and vice versa.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
+    Operation:Invert
+    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:InvertedRed
+    Image or measurement?:Image
+    Select the first image:OrigRed
+    Multiply the first image by:1.0
+    Measurement:
+    Image or measurement?:Image
+    Select the second image:
+    Multiply the second image by:1.0
+    Measurement:
+
+IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\'Identify the individual cells. Three-class thresholding works better than the default two-class method. Some adjustment of the correction factor, 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:InvertedRed
+    Name the primary objects to be identified:Cells
+    Typical diameter of objects, in pixel units (Min,Max):5,9999
+    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:Intensity
+    Size of smoothing filter:4
+    Suppress local maxima that are closer than this minimum allowed distance:4
+    Speed up by using lower-resolution image to find local maxima?:Yes
+    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
+    Handling of objects if excessive number of objects identified:Continue
+    Maximum number of objects:500
+    Use advanced settings?:Yes
+    Threshold setting version:10
+    Threshold strategy:Global
+    Thresholding method:Otsu
+    Threshold smoothing scale:1.3488
+    Threshold correction factor:0.8
+    Lower and upper bounds on threshold:0.0,1.0
+    Manual threshold:0.0
+    Select the measurement to threshold with:None
+    Two-class or three-class thresholding?:Three classes
+    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
+
+OverlayOutlines:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Overlay the cell outlines in the inverted image.\'\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:InvertedRed
+    Name the output image:InvertedRedOutlines
+    Outline display mode:Color
+    Select method to determine brightness of outlines:Max of image
+    How to outline:Inner
+    Select outline color:Red
+    Select objects to display:Cells
+
+MeasureObjectNeighbors:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Obtain neighborhood metrics by expanding the cells until they touch. Retain an output image for later export.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
+    Select objects to measure:Cells
+    Select neighboring objects to measure:Cells
+    Method to determine neighbors:Expand until adjacent
+    Neighbor distance:5
+    Retain the image of objects colored by numbers of neighbors?:Yes
+    Name the output image:ColorNeighbors
+    Select colormap:hot
+    Retain the image of objects colored by percent of touching pixels?:No
+    Name the output image:PercentTouching
+    Select colormap:Default
+
+MeasureObjectSizeShape:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\'Measure morphological features from the cell objects.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
+    Select objects to measure:Cells
+    Calculate the Zernike features?:No
+
+MeasureObjectIntensity:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure intensity features from cell objects against the red channel.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
+    Hidden:1
+    Select an image to measure:OrigRed
+    Select objects to measure:Cells
+
+SaveImages:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\'Save the neighbor output image as an 8-bit TIF, appending the text \\xe2\\x80\\x98colorneighbors\\xe2\\x80\\x99 to the original filename of the color image.\'\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:ColorNeighbors
+    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?:Yes
+    Text to append to the image name:_ColorNeighbors
+    Saved file format:png
+    Output file location:Default Output Folder\x7C
+    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...\x7C
+
+SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\'Save the overlay image as an 8-bit TIF, appending the text \\xe2\\x80\\x98invertedred\\xe2\\x80\\x99 to the original filename of the color image.\'\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:InvertedRedOutlines
+    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?:Yes
+    Text to append to the image name:_InvertedRed
+    Saved file format:png
+    Output file location:Default Output Folder\x7C
+    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...\x7C
+
+ExportToSpreadsheet:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:12|show_window:True|notes:\x5B\'Export any measurements to a comma-delimited file (.csv). The measurements made for the cell objects and the image will be saved to separate .csv files. Mean per-image cell measurements will also be exported to the image .csv.\'\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
+    Select the measurements to export:No
+    Calculate the per-image mean values for object measurements?:Yes
+    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?:No
+    Press button to select measurements:
+    Representation of Nan/Inf:NaN
+    Add a prefix to file names?:No
+    Filename prefix:MyExpt_
+    Overwrite existing files without warning?:Yes
+    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:Cells
+    Combine these object measurements with those of the previous object?:No
+    File name:DATA.csv
+    Use the object name for the file name?:Yes