Hello, dear colleagues.
My name is Eugen, i’m pathologist and want correlate expression level of some IHC and density of CT
and/or PET by prostata cancer.
Could you please help me with:
distinguishing aseals with prostatic adenocarcinom from normal tissue?
Should i use “cell detection” and then “object classifier” with some different parameter? I already seen teaching video about using “nuclear/cytoplasm ratio”,but measurement of nuclear/cytoplasm ratio didn’t help me because for prostata CA exist another criteria: prominent nucleoli and absend of basal membrane…
Is it normal that QuPath take up to 50-60 Gb aktiv memory at the time of cell detetion?
My alternative variant was mark tumor and non tumor manually and then run pixel classifyer…but pixel classifyer work on the whole slide and analyse also areals, then i don’t want (nesty place).
But generally it’s looks in the way they i want!
Could i run pixel classifyer just on some ROI from annotation and not on the whole slide?
Or like alternative way after pixel classification ignore areals without tumor - it will be also nice
I add some pictures to visualize my problem
Thank you in advance!