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      Cellpose: a generalist algorithm for cellular segmentation

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          Abstract

          <p class="first" id="d5581529e85">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. </p>

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          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Deep Residual Learning for Image Recognition

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              Matplotlib: A 2D Graphics Environment

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Nature Methods
                Nat Methods
                Springer Science and Business Media LLC
                1548-7091
                1548-7105
                December 14 2020
                Article
                10.1038/s41592-020-01018-x
                33318659
                4e2eee88-8362-4a05-8907-7f20e82b4cda
                © 2020

                http://www.springer.com/tdm

                http://www.springer.com/tdm

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