QuPath script that segments two populations of cells based on different channels

segment-twice.groovy

import static qupath.lib.scripting.QPEx.*
    
setImageType('FLUORESCENCE');
// ch1 = A594 (PVcells, cell staining), ch2 = a488 (cell stain to identify brain region layer 4, do not quantify), ch3 = dapi

// make sure all regions are selected so measurments are performed within them
selectAnnotations();

// run the watershed segmentation on channel 4 (dapi), get nucleus and cell objects
runPlugin('qupath.imagej.detect.nuclei.WatershedCellDetection', '{"detectionImageFluorescence": 3,  "requestedPixelSizeMicrons": 0.0,  "backgroundRadiusMicrons": 8.0,  "medianRadiusMicrons": 0.0,  "sigmaMicrons": 1.5,  "minAreaMicrons": 10.0,  "maxAreaMicrons": 400.0,  "threshold": 100.0,  "watershedPostProcess": true,  "cellExpansionMicrons": 5.0,  "includeNuclei": true,  "smoothBoundaries": true,  "makeMeasurements": true}');

// Create the classes
negative = getPathClass('Negative', getColorRGB(0,0,255)) // blue
ch1Positive = getPathClass('Ch1 positive', getColorRGB(0, 255, 0)) // green

// Get cells & reset all the classifications
def allcells = getCellObjects()
resetDetectionClassifications()

allcells.each {it.setPathClass(negative)}

// run the watershed segmentation on channel 1(dapi) to get the positive cells
runPlugin('qupath.imagej.detect.nuclei.WatershedCellDetection', '{"detectionImageFluorescence": 1,  "requestedPixelSizeMicrons": 0.0,  "backgroundRadiusMicrons": 8.0,  "medianRadiusMicrons": 0.0,  "sigmaMicrons": 1.5,  "minAreaMicrons": 10.0,  "maxAreaMicrons": 400.0,  "threshold": 400.0,  "watershedPostProcess": true,  "cellExpansionMicrons": 0.1,  "includeNuclei": true,  "smoothBoundaries": true,  "makeMeasurements": true}');

def pvcells = getCellObjects()
resetDetectionClassifications()

pvcells.each {it.setPathClass(ch1Positive)}

// update number of positives to avoid overcounting
// ch1Pos = cells.findAll {it.getPathClass().getName() == "Ch1 positive"}

fireHierarchyUpdate();

print allcells.size()
print pvcells.size()

// Create Output Path for results
path = buildFilePath(PROJECT_BASE_DIR, 'export')

// Make directory in case the directory does not exist
mkdirs(path)

// Make a results filename
fileName = 'classification-result.txt'

// Write results to a file
file = new File(path, fileName)
delimiter = '\t'

//  check if the file exists and if not, create it
if(!file.exists() ) {
    file.text = 'Image Name' << delimiter <<
               'Ch1 Positive' << delimiter <<
               'Total Cells' << delimiter << System.lineSeparator()
}

// Append the results to the end of the file
file << getProjectEntry().getImageName() << delimiter <<
       pvcells.size() << delimiter <<
       allcells.size() << delimiter << System.lineSeparator()