Mastodawn

We are happy to announce the "NL-BioImaging Advanced Image Analysis course for biologists", 21–24 September at Erasmus MC in Rotterdam.

More info and register here:
https://courses.microscopie.nl/?event_id=527

Four hands-on days going from segmentation to full pipelines:
- Automate reproducible image processing with Jupyter notebooks
- Assess classical and AI approaches for specific challenges
- Train and optimise deep learning models on biological datasets
- Document and publish reproducible image analysis pipelines

Guidance from experts across the Netherlands.
Limited to 20 participants.

Feel free to tag colleagues or share this with your network!

#bioimaging #imageanalysis #course #openscience
#cellpose #OMERO #Jupyter #napari #FAIRdata
NL-Bioimaging #NVvM

Kemal Yaylali May 8

Just shipped v0.1.0 of CellSeg — an open-source Android app that runs Cellpose cell segmentation on-device

Free, MIT-licensed, no ads, no tracking. ~14 MB cyto3 ONNX model, runs fully offline after first download. Optional Cellpose-SAM cloud fallback via Hugging Face for harder cases

Written up the launch with the architecture, the licensing chapter, and an honest list of what v0.1.0 isn't yet:

https://kemal.yaylali.uk/cellseg-v0-1-0-is-out/

#bioinformatics #microscopy #cellpose #android #buildinpublic #opensource

Kemal May 8

CellSeg v0.1.0 is out

Three months ago I started building an Android app to count cells. Today the first public build is up, and I'd like to introduce it properly. It's called CellSeg, and it does one stubborn small thing: it lets a bench scientist take a picture of a microscopy sample with their phone and get a cell count out of it in a few seconds, on the device, with no laptop and no cloud round-trip in the way. You can install it from cellseg.yaylali.uk. It's an open-source sideload APK — not on the Play […]

https://kemal.yaylali.uk/cellseg-v0-1-0-is-out/

Kemal May 7

The bench problem that wouldn’t go away

CellSeg There's a thing that happens in a tissue culture lab that I've never quite gotten used to, even after years of doing it. You pull a sample from a flask or a bioreactor, you load it onto a counting chamber, you put your eye to the microscope, and then you spend the next several minutes squinting at a grid and clicking a tally counter. If you're being honest about it, the count you write down is half measurement, half judgement call. Were those two cells one cell that was dividing? Was […]

https://kemal.yaylali.uk/the-bench-problem-that-wouldnt-go-away/

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Fabrizio Musacchio Jul 13, 2025

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 👌

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Fabrizio Musacchio Jul 13, 2025

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.

Fabrizio Musacchio Jul 11, 2025

✍️ 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.

🌍 https://elifesciences.org/articles/99848

#DeepLearning #Neuroscience

CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy

Self-supervised deep learning models can accurately perform 3D segmentation of cell nuclei in complex biological tissues, enabling scalable analysis in settings with limited or no ground truth annotations.

eLife

CSBJ May 27, 2025

🤖 How resilient are deep learning models to real-world imperfections in microscopy?

🔗 Practical guidelines for cell segmentation models under optical aberrations in microscopy. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2024.09.002

📚 CSBJ Quantum Biology and Biophotonics: https://www.csbj.org/qbio

#CellSegmentation #Microscopy #AIinBiology #OpticalAberrations #DeepLearning #BiomedicalAI #Cellpose #ComputationalBiology #PLCM

AI4Life Apr 26, 2024

After a day and a half of #ImageAnalysis in the cloud with #dask by the IDR team, Damian Dalle Nogare takes over a practical session to apply several #AI models for cell segmentation using #Cellpose

Carsen Stringer (she/her)Apr 8, 2024

Despite all their hype in AI, we found that Transformers do not outperform #Cellpose for cellular segmentation tasks: https://www.biorxiv.org/content/10.1101/2024.04.06.587952v1

#bioinformatics #biology #machinelearning