The multimodality cell segmentation challenge: towards universal solutions

Ma, J., Xie, R., Ayyadhury, S., Ge, C., Gupta, A., Gupta, R., Gu, S., Zhang, Y., Lee, G., Kim, J., Lou, W., Li, H., Upschulte, E., Dickscheid, T., de Almeida, J., Wang, Y., Han, L., Yang, X., Labagnara, M., Gligorovski, V., Scheder, M., Rahi, S., Kempster, C., Pollitt, A. ORCID: https://orcid.org/0000-0001-8706-5154, Espinosa, L., Mignot, Y., Middeke, J., Eckardt, J.-N., Li, W., Li, Z., Cai, X., Bai, B., Greenwald, N., Van Valen, D., Weisbart, E., Cimini, B., Cheung, T., Bruck, O., Bader, G. and Wang, B. (2024) The multimodality cell segmentation challenge: towards universal solutions. Nature Methods. ISSN 1548-7105 doi: 10.1038/s41592-024-02233-6

Abstract/Summary

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/115930
Identification Number/DOI	10.1038/s41592-024-02233-6
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences
Publisher	Nature
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