To wrap this up: Both tools are easy to test. I 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. Curious what impressions and feedback the students will share. 🧪🔍

Raw image (3D)

Cellpose segmentation and applied parameters

CellSeg_3D segmentation and applied parameters

CellSeg_3D post-hoc instant segmentation and applied parameters

Tested both tools also on a more realistic 3D stack from the #ImageJ sample library. #Cellpose is fast and works well out of the box. #CellSeg3D is, however, slower and needs some further tuning for instance segmentation I guess — but looks promising! Definitely worth a try 👌 #BioimageAnalysis

3D view: raw data (upper left), Cellpose segmentation (upper right), CellSeg3D segmentation (lower left)

Cellpose segmentation and applied parameters

CellSeg_3D segmentation and applied parameters

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.

✍️ 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.
@napari.org

👀Proofs! Excited to see #CellSeg3D formally out soon!

Big congrats to first author Cyril Achard et al 🎉

🥳Alrighty - end-of-year push to get the final version of #CellSeg3D out!

👩‍💻 pip install napari-cellseg3d==0.2.2
📚 biorxiv.org/content/10.1...

We extended benchmarking, new videos, extended text, and a demo on cFOS 👋