Improve pixel classifier measurement performance

[petebankhead]

Also fixes a possible bug when making measurements from point annotations.
Inspired by this discussion: https://forum.image.sc/t/qupath-measure-pixel-classifier-area-per-cell-detection-for-wsis/72701

[petebankhead]

I tested performance using CMU-1.svs.
I used a very basic thresholder and simple classifier trained for 3 classes, saved for both classification and probability output - then ran the script at the bottom.

Using a Mac Studio (2022) with M1 Max and 32 GB RAM the processing time was:

v0.3.0	v0.4.0-SNAPSHOT
593.9 s	60.1 s

Results identical as far as I can tell. So... quite a substantial difference :)

Cell detection took close to 30s, with 326 498 cells detected,.

def checkpoints = [:]


setImageType('BRIGHTFIELD_H_E')
setColorDeconvolutionStains('{"Name" : "H&E default", "Stain 1" : "Hematoxylin", "Values 1" : "0.65111 0.70119 0.29049", "Stain 2" : "Eosin", "Values 2" : "0.2159 0.8012 0.5581", "Background" : " 255 255 255"}')

clearAllObjects()

checkpoints << ['Tissue detection': System.currentTimeMillis()]

createAnnotationsFromPixelClassifier("Tissue detection", 10000.0, 0.0, "INCLUDE_IGNORED")

checkpoints << ['Cell detection': System.currentTimeMillis()]

selectAnnotations()
runPlugin('qupath.imagej.detect.cells.WatershedCellDetection', '{"detectionImageBrightfield": "Hematoxylin OD",  "requestedPixelSizeMicrons": 1.0,  "backgroundRadiusMicrons": 8.0,  "medianRadiusMicrons": 0.0,  "sigmaMicrons": 1.5,  "minAreaMicrons": 10.0,  "maxAreaMicrons": 400.0,  "threshold": 0.1,  "maxBackground": 2.0,  "watershedPostProcess": true,  "cellExpansionMicrons": 5.0,  "includeNuclei": true,  "smoothBoundaries": true,  "makeMeasurements": true}')

for (classifier in ['Some probability', 'Some classification']) {

    // Create annotation measurements
    checkpoints << ["Annotation measurements for $classifier": System.currentTimeMillis()]
    selectAnnotations()
    addPixelClassifierMeasurements(classifier, classifier)
    
    // Create cell measurements
    checkpoints << ["Cell measurements for $classifier": System.currentTimeMillis()]
    selectCells()
    addPixelClassifierMeasurements(classifier, classifier)
}
checkpoints << ["Done": System.currentTimeMillis()]
resetSelection()
println 'Done!'

def entries = checkpoints.entrySet() as List
println "Total time: \t${entries[-1].value - entries[0].value} ms"
for (int i = 0; i < entries.size()-1; i++) {
    println "    ${entries[i].key} \t${entries[i+1].value - entries[i].value}"
}

[petebankhead]

Latest commit adds more options to restrict where live pixel classifier prediction is calculated.

Previously, it could be restricted to annotations - but using their full bounding box. This could sometimes still result in very large regions being processed.

Now it's also possible to restrict using the annotation ROI directly (i.e. the ROI shape intersects the tiled region that may be processed). This can reduce the amount of processing required substantially in some cases.

Both options still exist, since the more complex calculations to restrict the predicted regions could potentially slow things down in some cases.