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

Genome biology 2026 Open access

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Authors: Thomas Defard, Alice Blondel, Sebastien Bellow, Anthony Coléon, Guilherme Dias de Melo, Florian Mueller, Thomas Walter
DOI: 10.1186/s13059-025-03908-9
Cited by: 2
References: 30

Abstract

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.

Fields of Study & Subjects

Single-cell and spatial transcriptomics RNA regulation and disease Cell Image Analysis Techniques

Publication details

Journal / source
Genome biology
Publisher
Springer Science and Business Media LLC
Volume
27
Issue
1
Publication date
30 January 2026
Language
EN