I am developing a pipeline to automate the detection of small, almost circular, bright blobs (4px) (see first image below) on high-resolution fluorescence images (2048px) and later to assign them to cells. The goal is to compute the number of blobs per cell
The training examples (in their simplest form) are 2-channel x 2048 x 2048 px images together with the position of the foci. The first channel contains the cell nuclei (see last image below) and the second channel is a marker for the blobs (see second image below).
- I would like to know what kind of neural network architecture addresses best the problem
- Which loss function to use
- Which metric to report
- The speed is not an issue
- It must discern blobs 1-2 pixels apart.
Thanks in advance for your opinions :)