I’d like to request some help identifying cells in a brightfield image (CellProfiler 2.0 r10997). I thought it would not be so difficult, as we are getting good contrast between cells and background. However, it has been fairly tough to get the cells identified without splitting some apart. The main issues I have encountered are:
the cells are very close, often touching, and the border marking the separation between them is often missed by the segmentation algorithm even when it is clear to the eye they should be separate (failure to separate clumps).
the interior of the cells contains some material that is about as intense as the background. The algorithms tend to call these pixels background and often divide a cell into two objects (over-segmentation).
cell diameters vary
Some problems I have encountered with some attempt to address these issues are:
smoothing (retaining edges) helps the problem of background-intensity pixels within cells, but results in a bit of object spreading that exacerbates the problem of cells being so close together.
“find dark holes” does not know the difference between “real” holes inside cells and apparent holes that show up when cells are clustered around a spot of background. That is, it does not only find convex shapes. Thus, using this to try to fill in holes within cells also degrades the background within tight cell clusters.
enhancing circles after edge-finding seems to mark the center of many cells, but the image also has many bright spots where diffuse signals from neighboring cells overlap. Also, it does not seem to handle the variation in cell diameter very well.
If anyone has a chance to take a crack at an object identification pipeline or offer any other advice, I’d be very grateful.
I have included a few representative images.
Ben Braun MD, PhD