I am a PhD student working on a project that uses fluorescent in situ hybridization in the postmortem human brain, and am fairly new to QuPath (using version 0.2.3). I am currently trying to analyze images taken at 20x on an Olympus FV1200 laser scanning confocal microscope.
Here is my current workflow: 1) Do a maximum intensity projection on the z-stack in Fiji, export it as a tif and then import that tif into QuPath. 2) Adjust the brightness/contrast of each channel. 3) use the positive cell detection on my DAPI channel to automatically identify cells. 4) use those cells as annotations to train object classifiers on other channels (to detect + or - cells). 5) Apply the trained classifiers across the whole project and obtain the density of the + cells in each image.
Here is my issue: sometimes the positive cell detection or object classifier will make mistakes (e.g., detect 2 or 3 nuclei as 1 nuclei, or classify a cell as positive when it is in fact negative). Despite playing around with the parameters, there seems to be misclassifications I cannot fix, probably due to the degraded nature of postmortem human brain tissue and because of the high background signal.
My question: is there a way to manually change a classification when I notice a mistake? For example, can I manually switch an annotation from a “negative” label to a “positive” label? Can I fix positive cell detections that encompass more than one cell manually? Any ideas of how I can resolve this problem? It would not be feasible to annotate all of the images manually.