I agree, these bright field images are quite hard to generalize the CP settings for. I tried and have these suggestions (see the attached pipeline):
(1) Try a new version of CP, the 2.0 version (NOT 2.1) which we call “2_0_Bugfix”:
I am suggesting this upgrade because I had better results with the MCT thresholding method in IdentifyPrimaryObjects and it is only available in newer version than the release. You can keep your old installation as a backup and save this one along side it. Just make sure that you indicate the version number when you save pipelines with the new version, because they are forward- but not backward-compatible.
(2) The main pipeline is still the three modules (LoadImages, UnixColors, and IDPrimObjs) however I added a few modules in various attempts at pre-processing the images. They were not that substantially more successful than the simple pipeline but you can see what my thought process was.
(3a) Now uses MCT Global.
(3b) Distinguish clumped objects with “Shape” method, and as well, uses the Shape method to “draw dividing lines”.
(3c) Manually set the “Suppress local maxima” distance to 13. Increasing this from the auto distance which was smaller seems to help.
(4) There are still spurious objects identified. I also added a sample MeasureObjectSizeShape and FilterObjects module to show you how you might filter out some of these objects, depending on what measure you think would best exclude “bad” objects.
** However my preferred solution to improve segmentation is to add a fluorescent marker! Nuclear markers are nice since they tend to not overlap (e.g. DAPI). Once you have a decent segmentation, you can use other measures to distinguish sperm cells from other objects. Of course you need a fluorescence scope, markers, etc, so I understand if you cannot or prefer not to.
PIPE_DLogan3_CP_2_0_Bugfix.cp (7.06 KB)