We are trying to count the number of cells positive for three different fluorescent markers, acquired on a confocal microscope at 32 bits. We are running into trouble with the autofluorescence and CellProfiler identifying too many false positive puncta, falsely inflating the number of positive cells.
I have tried all the different threshold methods, illumination correction, subtracting out a percentage of the intensity of our negative control image, thresholding twice, smoothing, enhancing speckles, etc. So far, the closest we’ve gotten to our hand-counted values is through subtracting the value of the intensity of the negative control image from the experimental images and then using IdentifyPrimaryObjects. Otherwise, even with maximum thresholding, IdentifyPrimaryObjects identifies way too many speckles (so they cover the entire image). The other way I have gotten the number of objects identified to a reasonable number is by rescaling the intensity of my images (stretch to the full spectrum) and then thresholding twice (once before IdentifyPrimaryObjects and once within IdentifyPrimaryObjects). Even still, I have to use RobustBackground and remove 90% of the lower outliers. However, these results are completely different from our hand-counted numbers, and we are worried that rescaling affects the relative amounts of positive fluorescent puncta in each image.
Essentially, I am at a loss. I came across some posts talking about CellProfiler acting strangely with 32-bit images, so I converted my images to 16-bit, but those results were not accurate either.
I hope I have been clear – any help is appreciated. I am happy to attach my various pipelines and example images if needed, and please let me know if there is anything I can clarify.