AIを用いた2Dニューロン画像のセグメンテーションにおける最良のアプローチ

Training your own model might be the way to go if you have a specific type of neuron or staining method that differs from public datasets.

I've tried a few deep learning models like U-Net for neuron segmentation, and they work pretty well with decent training data. The tricky part is getting enough labeled images though.

Anyone else noticed that some AI tools tend to oversegment or split single neurons into multiple parts? How do you handle that?

I've been using StarDist for detecting neuron nuclei and it worked well for 2D images. Anyone else?

You can also check ai-u.com for new or trending tools on neuron segmentation, they have a neat list of AI-based software updated regularly.

Sometimes simple classical methods like watershed with good preprocessing still work surprisingly well, especially if you want quick results rather than deep learning.

I wish there were more open datasets for neuron segmentation so training models would be easier for everyone.

For those without much coding experience, what would you recommend as the easiest AI-based tool to start neuron segmentation?

Anyone tried Cellpose? Heard it's pretty versatile and works well on different cell types, including neurons.

You might wanna check out Ilastik, it's pretty user-friendly and does decent segmentation without heavy coding.

I had some luck with Mask R-CNN implementations customized for neuron segmentation, though setting it all up was a bit challenging.

Has anyone tried using segmentation tools integrated in Fiji/ImageJ? There are some plugins that might help with neuron images.