Evidence map›Paper›PMID 41618375›Full record

ArticleGenome biology2026

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, Thomas Walter

Abstract read
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Thomas Defard *Center for Computational Biology, Mines Paris PSL, 60 Boulevard Saint Michel, 75006, Paris, France.
Alice Blondel *Center for Computational Biology, Mines Paris PSL, 60 Boulevard Saint Michel, 75006, Paris, France.
Sebastien BellowInstitut Pasteur, Université Paris Cité, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), Paris, France.
Anthony ColeonInstitut Pasteur, Université Paris Cité, Lyssavirus Epidemiology and Neuropathology Unit, Paris, France.
Guilherme Dias de MeloInstitut Pasteur, Université Paris Cité, Lyssavirus Epidemiology and Neuropathology Unit, Paris, France.
Florian MuellerInstitut Pasteur, Université Paris Cité, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), Paris, France. florian.muller@pasteur.fr.
Thomas WalterCenter for Computational Biology, Mines Paris PSL, 60 Boulevard Saint Michel, 75006, Paris, France. Thomas.Walter@minesparis.psl.eu.

Funding

Agence Nationale de la Recherche ANR-24-EXCI-0001Agence Nationale de la Recherche ANR-24-EXCI-0002Agence Nationale de la Recherche, France ANR-24-EXCI-0001Fondation pour la Recherche Médicale ANRS MIE202112015304Grand Équipement National De Calcul Intensif, France AD011015678
6 · The paper itself

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.

Indexed as

AlgorithmsImage Processing, Computer-AssistedAnimalsHumansRNASpatial TranscriptomicsRNACell segmentationDeep learningSpatial transcriptomics

Identifiers

PMID41618375
PMCPMC12934110

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.