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Cellpose 2.0: how to train your own model

View ORCID ProfileCarsen Stringer, View ORCID ProfileMarius Pachitariu
doi: https://doi.org/10.1101/2022.04.01.486764
Carsen Stringer
HHMI Janelia Research Campus, Ashburn, VA, USA
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  • For correspondence: stringerc@janelia.hhmi.org pachitarium@janelia.hhmi.org
Marius Pachitariu
HHMI Janelia Research Campus, Ashburn, VA, USA
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  • For correspondence: stringerc@janelia.hhmi.org pachitarium@janelia.hhmi.org
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Abstract

Generalist models for cellular segmentation, like Cellpose, provide good out-of-the-box results for many types of images. However, such models do not allow users to adapt the segmentation style to their specific needs and may perform sub-optimally for test images that are very different from the training images. Here we introduce Cellpose 2.0, a new package which includes an ensemble of diverse pretrained models as well as a human-in-the-loop pipeline for quickly prototyping new specialist models. We show that specialist models pretrained on the Cellpose dataset can achieve state-of-the-art segmentation on new image categories with very little user-provided training data. Models trained on 500-1000 segmented regions-of-interest (ROIs) performed nearly as well as models trained on entire datasets with up to 200,000 ROIs. A human-in-the-loop approach further reduced the required user annotations to 100-200 ROIs, while maintaining state-of-the-art segmentation performance. This approach enables a new generation of specialist segmentation models that can be trained on new image types with only 1-2 hours of user effort. We provide software tools including an annotation GUI, a model zoo and a human-in-the-loop pipeline to facilitate the adoption of Cellpose 2.0.

Competing Interest Statement

The authors have declared no competing interest.

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 April 05, 2022.
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Cellpose 2.0: how to train your own model
Carsen Stringer, Marius Pachitariu
bioRxiv 2022.04.01.486764; doi: https://doi.org/10.1101/2022.04.01.486764
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Cellpose 2.0: how to train your own model
Carsen Stringer, Marius Pachitariu
bioRxiv 2022.04.01.486764; doi: https://doi.org/10.1101/2022.04.01.486764

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