Setting up Ilastik to segment tiny, clustered objects

Greetings good people!

I am trying to segment pictures with very tiny, circular features. The problem is pretty much the same as in my older thread (this one, meaning that the pictures are noisy and I can’t afford to use any kind of gaussian or median filtering because they would “mix” very close circles together. Some circles have a diameter of a few pixels, even.

I’ll post here an example of the pictures I’m trying to work with (for reference, they are 800x800 px wide):

However, since last time I managed to make some progress on the detection side, meaning that now the problem is just ("“just”") segmenting the pictures properly.

I have been having quite some success with Ilastik and its pixel-classification mode, but I still need to fine-tune the process, as the results are still not entirely usable, albeit very good.
To be more clear, this is the result I get after training on five similar, 2D pictures on the example above:

As you may see, Ilastik struggles to separate circles that are very close one to each other.

My question is: what can I do to optimize the process? For very small and clustered circles are there some training features that I should turn off or on? Should I limit the features to smaller sigmas? Is there anything in particular that I should take care of during the training?

Thank you in advance!

Sounds like your images are a good candidate for deep learning segmentation.
Did you try Weka segmentation plugin in Fiji.

have you tried stardist (link to stardist video)? It might work right out of the box for you and can cope with overlapping objects.

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I did! But Ilastik gave me better results on my data (maybe because Ilastik’s tools are easier to use on my pictures)

I can give it a look, but I’d rather not change software, especially because I built my workflow around it and I invested quite some time on training

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