Hi QuPath Community,
I am attempting to use positive cell detection to count cytoplasmic neuronal inclusions with a DAB stain. My images, however, have various artefactual stainings which I am having trouble getting QuPath to differentiate between. My method has been to use the built-in positive cell detection program on the entire image (not just a smaller region of interest). I try to optimize parameters manually for a single image, then further optimizing the parameters for another image, and then for a third one, and then assessing overall precision on the entire image set. I have had issues with this method, as some of the images are particularly messy and have false positives while other images don’t pick up enough positive cells. My issues may be coming from not optimizing the right parameters. I have found that increasing sigma removes some artefacts from a single image, but it also removes some positive cells which can be detected again by decreasing the threshold or the minimum nucleus area. Then, false positives can be reduced by changing the intensity threshold value. When I try the parameters out on a different image, however, there may be many false negatives, in which case I reduce the sigma and increase the threshold/minimum nucleus area. Thus far, I have found that I can optimize the parameters for a single image such that all of the inclusions are counted, but those parameters do not work sufficiently for all of the other images. Is there a way that I could be doing this method better or is there a better method than this for counting cytoplasmic inclusions in images with many artefacts? Perhaps one that involves coding or classifying cells vs artefacts?
Unfortunately, it appears that I am not able to show sample images due to image.sc policy that new users cannot upload images to posts.