ArticleNature methods2021
Cellpose: a generalist algorithm for cellular segmentation.
Article in Nature methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2,356 papers, 1 of them a synthesis that pooled it.
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Who cites it
2,356 citing papers in PubMed, 1 synthesis or guideline pooled it.
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Authors and funding
4 authors.
Funding
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 method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.
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