I have a large dataset of 21,120 co-culture images with over 4000 cells per image (so annotating seems impossible). I need help to figure out the best way to segment them.
I made a pipeline to IdentifyPrimaryObjects and to measure granularity, intensity & shape and sizes etc in CellProfiler. Then I created a properties file and used CPA, to separate the co-culture nuclei/cells in CellProfiler Analyst by selecting over 160 positive and negative examples.
But when I run classify, though it does decently identify the nuclei, it counts the same nuclei multiple times. I don’t know how I can improve the current results (screenshot attached).
My main objective is to get correct counts for each type of nuclei in the co-culture.
Any suggestions would be helpful right now.
Pipeline I used - coculture_Manual_overlay_CPA_2.cpproj (418.2 KB)