PT  - JOURNAL ARTICLE
AU  - Carsen Stringer
AU  - Michalis Michaelos
AU  - Marius Pachitariu
TI  - Cellpose: a generalist algorithm for cellular segmentation
AID  - 10.1101/2020.02.02.931238
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.02.02.931238
4099  - http://biorxiv.org/content/early/2020/02/03/2020.02.02.931238.short
4100  - http://biorxiv.org/content/early/2020/02/03/2020.02.02.931238.full
AB  - 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 algorithm called Cellpose, which can very precisely segment a wide range of image types out-of-the-box 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. 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.