Pré-Publication, Document De Travail Année : 2025

RNA2seg: a generalist model for cell segmentation in image-based spatial transcriptomics

Thomas Defard
Alice Blondel
Sebastien Bellow
Anthony Coleon
Guilherme Dias de Melo
Florian Mueller

Résumé

Abstract Imaging-based spatial transcriptomics (IST) enables high-resolution spatial mapping of RNA species. A key challenge in IST 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. The method is available as a documented pip package: https://github.com/fish-quant/rna2seg .

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hal-05230880 , version 1 (30-08-2025)

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Thomas Defard, Alice Blondel, Sebastien Bellow, Anthony Coleon, Guilherme Dias de Melo, et al.. RNA2seg: a generalist model for cell segmentation in image-based spatial transcriptomics. 2025. ⟨hal-05230880⟩
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