Fabrizio Musacchio✍️ New in #eLife: #CellSeg3D introduces #WNet3D, a self-supervised 3D #segmentation method for #microscopy data — no labels needed. Claims to outperform #Cellpose/#StarDist on 4 datasets. Includes #opensource plugin (#Napari) + full 3D annotated #cortex dataset. Will test it later.
Tested #CellSeg3D and #Cellpose on their example c5image dataset. Both segmentations look reasonable out-of-the-box, without any deep parameter tuning. With some extra effort, one could likely push either further I guess. Overall, both tools perform quite well on this small sample data set.
Tried the same with a more realistic 3D stack from the #ImageJ sample library.#Cellpose runs fast and segments very well out of the box.#CellSeg3D takes considerably longer and seems to segment decently, but I couldn’t get a proper instance #segmentation in the post-processing step (which is recommended as part of its workflow). However, #CellSeg3D looks very promising — just needs some more time and parameter exploration, I guess.
I’d recommend giving it a try 👌
To wrap this up: Both tools are easy to test. I highly recommend trying them on your own data to see what works best for your use case.
I’ll include #CellSeg3D in our next #Napari #bioimage analysis course (https://www.fabriziomusacchio.com/teaching/teaching_bioimage_analysis/). Curious what impressions and feedback the students will share. 🧪🔍
What I really like about @napari is how well it integrates modern #Python tools. Great to have such a flexible, evolving #opensource platform for (bio) #imageanalysis! 👌
