Seeds Classification

[atombysx]

I’m currently running MIN1PIPE on Miniscope videos recorded in the VTA. As VTA is too deep, it has a smaller field of view and a bulb of light covering the centre of the video.

Hence, I used a 24 structural element size and it worked well.

However, when it comes to seeds classification, it identified many seeds for only a few overlapping neurons at the end. (See image attached)

I wonder if you have encountered this before. Is there a way to work around this?

My thoughts are to either train a new RNN for this setting or run CNMF again on the data_processed.m by roifn * sigfn.

Many thanks

[JinghaoLu]

Hi sorry for the really late reply. To solve this problem, first an improved signal to noise ratio will certainly help. Second, I suggest you spatially downsample the video as much as possible so that you can use SE size within the range of [3,7]. The root of this issue, however, is still the low signal level within the FOV, so that the algorithm cannot tell the neurons from the background.