Cellpose: a generalist algorithm for cellular segmentation
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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 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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The Cellpose default model ('cyto') was trained on 540 images of cells and objects with one or two channels (if the image had a nuclear channel).
“The Cellpose default model (‘cyto’) was trained on 540 images of cells and objects with one or two channels (if the image had a nuclear channel).”
From Cellpose 2.0: how to train your own model (2022) · cites this paper as [5]
Cellpose machine learning was unable to segment the full cell including its processes.
“we found that state-of-the-art Cellpose machine learning β was unable to segment the full cell including its processes (Stringer et al, 2021).”
From APOE from astrocytes restores Alzheimer’s Aβ-pathology and DAM-like responses in APOE deficient microglia (2024) · cites this paper as Stringer et al, 2021
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- Authors
- Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M.
- Affiliations
- Howard Hughes Medical Institute (US)
- Corresponding author
- Pachitariu, Marius
- Venue
- Nature Methods
- Published
- 2020-12-14
- Volume
- 18
- Issue
- 1
- Pages
- 100-106
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W3111521801
- Cited by
- 4,369
- References
- 8
- Citations by year
- 2026: 1095 · 2025: 1208 · 2024: 906 · 2023: 597 · 2022: 353 · 2021: 179 · 2020: 28 · 2018: 1 · 2017: 1 · 2012: 1
- FWCI
- 138.85
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute (Janelia)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- Cellpose, cell segmentation, nuclei segmentation, cell membrane segmentation, microscopy image analysis, 3D segmentation, transfer learning
- MeSH terms
- Deep Learning, Algorithms, Animals, Cell Nucleus, Female, Humans, Image Processing, Computer-Assisted, Male, Mice, Inbred C57BL, Neurons, Software, Neural Networks, Computer, Mice
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