[Complex Workflow Demo] A demonstration of how a a QuPath workflow can become a script for complex analysis of multiplex data #QuPath #workflow #demo

Complex Workflow Demo.groovy

// This workflow represents the end result that can be obtain by following Dr. Sara McArdle's "Running a complex project in QuPath" tutorial at https://www.youtube.com/watch?v=7AvuzAgHk5o
// @author Olivier Burri

// Prepare the image type
setImageType('FLUORESCENCE')

// Clean up the current image data, as we assume we will be starting from scratch
clearAllObjects()

// Directly create detections from a previously created pixel classifier called "Ck"
// This classifier will create two annotations of type "Tumor" and "Region*"
createAnnotationsFromPixelClassifier("Ck", 120.0, 120.0, "INCLUDE_IGNORED")

// Segment cells in the "Tumor" annotation with one set of cell detection settings
selectObjectsByClassification("Tumor")

runPlugin('qupath.imagej.detect.cells.WatershedCellDetection', '{"detectionImage": "DAPI",  "requestedPixelSizeMicrons": 0.498,  "backgroundRadiusMicrons": 20.0,  "medianRadiusMicrons": 0.0,  "sigmaMicrons": 1.5,  "minAreaMicrons": 10.0,  "maxAreaMicrons": 400.0,  "threshold": 2.0,  "watershedPostProcess": true,  "cellExpansionMicrons": 5.0,  "includeNuclei": true,  "smoothBoundaries": true,  "makeMeasurements": true}')

// Segment cells in the "Region*" annotation with another cell detection setting, as the nuclei morphology is different
selectObjectsByClassification("Region*")

runPlugin('qupath.imagej.detect.cells.WatershedCellDetection', '{"detectionImage": "DAPI",  "requestedPixelSizeMicrons": 0.498,  "backgroundRadiusMicrons": 0.0,  "medianRadiusMicrons": 1.0,  "sigmaMicrons": 1.0,  "minAreaMicrons": 10.0,  "maxAreaMicrons": 400.0,  "threshold": 1.5,  "watershedPostProcess": true,  "cellExpansionMicrons": 5.0,  "includeNuclei": true,  "smoothBoundaries": true,  "makeMeasurements": true}')


// Add Haralick features to the detected cells, needed for the Cd68 cell classifier
selectDetections()

runPlugin('qupath.lib.algorithms.IntensityFeaturesPlugin', '{"pixelSizeMicrons": 0.498,  "region": "ROI",  "tileSizeMicrons": 25.0,  "channel1": true,  "channel2": true,  "channel3": true,  "channel4": true,  "channel5": true,  "channel6": false,  "channel7": false,  "channel8": false,  "doMean": false,  "doStdDev": false,  "doMinMax": false,  "doHaralick": true,  "haralickMin": 0.0,  "haralickMax": 30.0,  "haralickDistance": 1,  "haralickBins": 32}');

// Add smoothed features to the detected cells, needed for the Cd68 cell classifier
runPlugin('qupath.lib.plugins.objects.SmoothFeaturesPlugin', '{"fwhmMicrons": 50.0,  "smoothWithinClasses": false}');

// Run the composite classifier on all cells to find all multi-cell types
runObjectClassifier("CD8 FoxP3 Cd68 PD1")

// Run the Pdl1 classifier on both regions, so as to have the Pd11 area for each
selectObjectsByClassification("Tumor", "Region*")

createAnnotationsFromPixelClassifier("Pdl1", 2.0, 2.0)

// Add the distances to each annotation for all the cells, which can provide useful information
selectDetections()

detectionToAnnotationDistances(true)