Cell segmentation currently involves the use of various bespoke algorithms designed for specific cell types, tissues, staining methods and microscopy technologies. We present a universal algorithm that can segment all kinds of microscopy images and cell types across diverse imaging protocols.
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References
Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019). A review article that highlights the great potential of deep learning for cell segmentation in microscopy images.
Stringer, C. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021). This paper presents a generalist model for cell segmentation.
Caicedo, J. C. et al. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat. Methods 16, 1247–1253 (2019). This paper demonstrates that international competition is an effective way to solve challenging biological tasks.
Maška, M. et al. The Cell Tracking Challenge: 10 years of objective benchmarking. Nat. Methods 20, 1010–1020 (2023). This paper summarizes the challenges of cell segmentation and tracking.
Ma, J. & Wang, B. Towards foundation models of biological image segmentation. Nat. Methods 20, 953–955 (2023). This commentary article describes a blueprint for developing foundation models for bioimage segmentation.
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This is a summary of: Ma, J. et al. The multimodality cell segmentation challenge: toward universal solutions. Nat. Methods https://doi.org/10.1038/s41592-024-02233-6 (2024).
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Creating a universal cell segmentation algorithm. Nat Methods 21, 950–951 (2024). https://doi.org/10.1038/s41592-024-02254-1
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DOI: https://doi.org/10.1038/s41592-024-02254-1