I’ve been having some trouble counting nuclei with IdentifyPrimaryObjects.
So, I’m looking at neural nuclei. There are a variety of sizes and intensities, as well as some layering, so counting them has been tough. However, I’m getting pretty good counts from Identify Primary Objects.
I’ve tried all of the possible combinations of ways to distinguish between clumped cells and draw lines between them and I’ve found two combinations that work rather well-- Laplacian of Gaussian with both Propagate line drawing and Intensity-based line drawing (the two pipelines are attached). I’m using the Otsu PerObject threshold.
However, both of these methods are double/triple counting the larger, dimmer nuclei. It’s doing well with small, layered cells, but the large dim ones, even when totally isolated, are getting counted a lot.
Both of these methods are also counting nuclei that are clearly touching the edge of the image, discarding some but not all nuclei touching the edge. (I do have the ‘Discard objects touching the border of the image’ checked).
I’ve tried using Shape methods of dividing clumped cells and it’s giving me the same problem. I’ve also tried using just Watershed and I’ve found it to be less accurate with what I’m working with.