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Cellpose: a generalist algorithm for cellular segmentation

View ORCID ProfileCarsen Stringer, View ORCID ProfileMichalis Michaelos, View ORCID ProfileMarius Pachitariu
doi: https://doi.org/10.1101/2020.02.02.931238
Carsen Stringer
1HHMI Janelia Research Campus, Ashburn, VA, USA
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  • For correspondence: stringerc@janelia.hhmi.org pachitarium@janelia.hhmi.org
Michalis Michaelos
1HHMI Janelia Research Campus, Ashburn, VA, USA
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Marius Pachitariu
1HHMI Janelia Research Campus, Ashburn, VA, USA
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  • ORCID record for Marius Pachitariu
  • For correspondence: stringerc@janelia.hhmi.org pachitarium@janelia.hhmi.org
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Abstract

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.

Footnotes

  • http://www.cellpose.org

  • http://www.github.com/MouseLand/cellpose

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted February 03, 2020.
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Cellpose: a generalist algorithm for cellular segmentation
Carsen Stringer, Michalis Michaelos, Marius Pachitariu
bioRxiv 2020.02.02.931238; doi: https://doi.org/10.1101/2020.02.02.931238
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Cellpose: a generalist algorithm for cellular segmentation
Carsen Stringer, Michalis Michaelos, Marius Pachitariu
bioRxiv 2020.02.02.931238; doi: https://doi.org/10.1101/2020.02.02.931238

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