Dear ImageJ community,
I am currently working with confocal stacks, in which the nuclei are stained in red and the signal of interest in green (GFP). There are a lot of nuclei in a single image, but I am only interested in those cells that express the green signal.
My current workflow is to perform a Difference of Gaussians, followed by a 3D Watershed on the red channel.
I perform background subtraction on the green channel and then apply a threshold to generate a binary mask, only accounting for the GFP-positive cells. Because of the image noise and due to cellular extensions the mask goes through a Particle Analyzer, excluding all particles that are below a certain size.
I then multiply the green mask with the 3D-Watershed of the red channel and then use the 3D Manager to count the cells.
The problem with this approach is, that I have to subjectively estimate the threshold for the green channel and the maximum size every particle can have each time I perform the count and it seems like this is causing major inconsistencies with the number of GFP-positive cells for each data set.
Does anybody have any suggestions on how this approach could be improved or are there any alternative approaches to count the number of GFP-positive cells in our image data you could recommend?
I have attached the raw GFP and RFP channels in a zip file and another zip file contains the final image that I use to count the GFP-positive cells.
Cell_count.zip (78.8 KB)
Raw_Data.zip (19.2 MB)
Thanks in advance for your help!