I sincerely hope to be posting this correctly as I could not find a beginner’s category and find myself getting lost in Image.sc repeatedly. It takes getting used to I suppose.
Currently, I have joined a project in a working group that deals with endocrinology and reproductive medicine. A part of the project deals with whole slide scans of tissue samples stained in H&E.
If I understand QuPath’s mode of operation correctly, classifiers can be trained to automatically classify cells after (semi-)automatic detection based on measurements like nucleus size or size ratio, color intensity and so forth. Right now I am trying to evaluate whether QuPath is also capable of being trained to differentiate cells by features like “nucleus position” - which would require QuPath to be able to differentiate between basal and luminal sides in a previously detected cell. Basically, there is a catalogue of features that needs to be applied to cells and results in a classification (lacking a better term) according to a scoring system.
I understand that these tasks are non-trivial and would require a lot of hard work and scripting. My question here for now is: Is something like this possible at all? Would at be possible with QuPath? If QuPath-savvy users / developers could tell me whether this project could be handled with QuPath at all, of course I will put a lot of time and effort into learning everything required and sharing my experiences with the community. However, since this is a limited funds / time project, I would rather spend resources differently if you - as the experts here - think that this approach is not feasible.
I hope you get my meaning, English is not my primary language and sometimes it’s hard for me to express my thoughts when writing…
Input is greatly appreciated. Thank you very much!!
PS, QuPath seems like a terrific piece of work!