KemalMay 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/

Haojia WuMay 27, 2025

proseg #cellseg #spatial #xenium #cosmx #merfish
https://github.com/dcjones/proseg

https://www.nature.com/articles/s41592-025-02697-0

GitHub - dcjones/proseg: Probabilistic cell segmentation for in situ spatial transcriptomics

Probabilistic cell segmentation for in situ spatial transcriptomics - dcjones/proseg

GitHub
Haojia WuMar 27, 2024
Save this for a later read:
The multimodality cell segmentation challenge: toward universal solutions #spatial #cellseg
https://www.nature.com/articles/s41592-024-02233-6
The multimodality cell segmentation challenge: toward universal solutions - Nature Methods

Cell segmentation is crucial in many image analysis pipelines. This analysis compares many tools on a multimodal cell segmentation benchmark. A Transformer-based model performed best in terms of performance and general applicability.

Nature
Haojia WuFeb 16, 2024
Cellpose V3 is out!
https://www.biorxiv.org/content/10.1101/2024.02.10.579780v1.full
#spatial #cellseg #pytorch #segmentation
Haojia WuJan 26, 2024
BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data #spatial #cellseg #bioinformatics #pytorch
https://www.nature.com/articles/s41467-023-44560-w
BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data - Nature Communications

Subcellular in situ spatial transcriptomics offers the promise to address biological problems that were previously inaccessible but requires accurate cell segmentation to uncover insights. Here, authors present BIDCell, a biologically informed, deep learning-based cell segmentation framework.

Nature