A cellular segmentation algorithm with fast customization
- PMID: 36344836
- DOI: 10.1038/s41592-022-01664-3
A cellular segmentation algorithm with fast customization
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Cellpose 2.0: how to train your own model.Nat Methods. 2022 Dec;19(12):1634-1641. doi: 10.1038/s41592-022-01663-4. Epub 2022 Nov 7. Nat Methods. 2022. PMID: 36344832 Free PMC article.
References
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- Caicedo, J. C. et al. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat. Methods 16, 1247–1253 (2019). This paper reports the results of the Data Science Bowl nuclear segmentation challenge, in which models trained on the most comprehensive image set performed best. - DOI - PubMed - PMC
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- Greenwald, N. F. et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40, 555–565 (2021). This paper introduces the Mesmer segmentation model and the TissueNet dataset, which contains over 1 million manually labeled cells from different tissues and fluorescent imaging platforms. - DOI - PubMed - PMC
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- Edlund, C. et al. LiveCell—a large-scale dataset for label-free live cell segmentation. Nat. Methods 18, 1038–1045 (2021). This paper introduces the LiveCell dataset, which contains over 1.6 million manually labeled cells in phase contrast images from various cell culture lines. - DOI - PubMed - PMC
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