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RNA2seg: a generalist model for cell segmentation in image-based spatial transcriptomics
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DOI：10.1186/s13059-025-03908-9.png)
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Imaging-based spatial transcriptomics enables high-resolution spatial mapping of RNA species. A key challenge in imaging-based spatial transcriptomics is accurate cell segmentation to assign each RNA molecule to the right cell. Here, we present RNA2seg, a novel segmentation algorithm trained on over 4 million cells from MERFISH and CosMx datasets across seven organs using a teacher-student training scheme. RNA2seg integrates RNA point clouds and all available membrane and nuclear stainings. Validation on manually annotated data shows superior performance including in zero-shot and few-shot settings.
Keyword:
Spatial transcriptomics
Cell segmentation
Deep learning
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