Hello fellow QuPath users.
I have been trying to train a pixel classifier to differentiate epithelium/tumour from stroma on H&E slides of adenomas which are pre-malignant polyps of the colon and rectum.
After having read through the info on github/readthedocs and watched tutorials, and with a lot of help from similar posts and comments in this forum (thanks btw!), i believe the classifier works well enough on at least some of my slides (or some parts of them), see the two pictures below with classifier off and on.
To help the classifier, i have added a third annotation “other”, which in this case is a type of cell (goblet cell) that produces and accumulates mucin which on the slide will look very similar to lumen, at least in color.
However, for other slides where maybe the color is a lot more similar between epithelial cells and the stroma, the classifier is having a hard time separating the two (see pictures below, same slide but different part of the slide). From the perspective of color i can understand that the program is struggling, but the spatial/geometric differences are rather distinct.
In this specific case, the classifier wrongly classifies areas of goblet cells (inside the circular epithelial/tumour patterns) as stroma, and in other cases (not shown) the classifier assigns areas of stroma with a lot of dark colored cells as epithelium/tumour.
So my question is this; is there a way for me to better enable QuPath to learn shapes and geometric patterns, or weigh this higher than it does currently? Is there a way for me to teach it some specific spatial rules?
- That tumour does not exist as small single cells or isolated areas in the area of stroma lying between two areas of tumour
- That stroma does not exist between tumour and goblet cells or lumen areas?
It does not seem to help the classifier to just add more training areas.
Furthermore, i have been testing the different features in combination with gaussian and/or gradient feature, and tried previewing them in the classifier dropdown menu to evaluate whether they would help the classifier. By far the greatest impact and help for the classifier was adding the gradient feature to the gaussian, and adding scales of 8.0.
I am aware that some slides simply might not be suited for automated analysis using image software due to homogeneity in color, or boundaries that are hard to define, but i still feel like i should be able to improve the classifier with all the features and options already at hand.
Any help will be greatly appreciated!