Rxivist logo

Cellpose: a generalist algorithm for cellular segmentation

By Carsen Stringer, Tim Wang, Michalis Michaelos, Marius Pachitariu

Posted 03 Feb 2020
bioRxiv DOI: 10.1101/2020.02.02.931238

Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. We trained Cellpose on a new dataset of highly-varied images of cells, containing over 70,000 segmented objects. We also demonstrate a 3D extension of Cellpose which reuses the 2D model and does not require 3D-labelled data. To support community contributions to the training data, we developed software for manual labelling and for curation of the automated results, with optional direct upload to our data repository. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.

Download data

  • Downloaded 4,812 times
  • Download rankings, all-time:
    • Site-wide: 859 out of 94,912
    • In bioinformatics: 140 out of 8,837
  • Year to date:
    • Site-wide: 244 out of 94,912
  • Since beginning of last month:
    • Site-wide: 344 out of 94,912

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News