{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T06:02:38Z","timestamp":1792648958179,"version":"4.5.0"},"posted":{"date-parts":[[2025,3,16]]},"group-title":"Bioinformatics","reference-count":34,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,3,16]],"date-time":"2025-03-16T00:00:00Z","timestamp":1742083200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,3,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  The accurate assignment of transcripts to their cells of origin remains the Achilles heel of imaging-based spatial transcriptomics, despite being critical for nearly all downstream analyses. Current cell segmentation methods are prone to over- and under-segmentation, misassign transcripts to cells, require manual intervention, and suffer from low sensitivity and scalability. We introduce segger, a versatile graph neural network based on a heterogeneous graph representation of individual transcripts and cells, that frames cell segmentation as a transcript-to-cell link prediction task and can leverage single-cell RNA-seq information to improve transcript assignments. On multiple Xenium dataset benchmarks, segger exhibits superior sensitivity and specificity, while requiring orders of magnitude less compute time than existing methods. The user-friendly open-source software implementation has extensive documentation (\n                  <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/elihei2.github.io\/segger_dev\/\">https:\/\/elihei2.github.io\/segger_dev\/<\/jats:ext-link>\n                  ), requires little manual intervention, integrates seamlessly into existing workflows, and enables atlas-scale applications.\n                <\/jats:p>","DOI":"10.1101\/2025.03.14.643160","type":"posted-content","created":{"date-parts":[[2025,3,16]],"date-time":"2025-03-16T23:35:16Z","timestamp":1742168116000},"source":"Crossref","is-referenced-by-count":20,"title":["Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data"],"prefix":"10.64898","author":[{"given":"Elyas","family":"Heidari","sequence":"first","affiliation":[{"name":"Artificial Intelligence in Oncology, German Cancer Research Center (DKFZ), Heidelberg, 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Informatics, Budapest University of Technology and Economics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikhita","family":"Pasnuri","sequence":"additional","affiliation":[{"name":"Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gleb","family":"Rukhovich","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Domenico","family":"Calafato","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Mathioudaki","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph M.","family":"Chan","sequence":"additional","affiliation":[{"name":"Human Oncology & Pathogenesis Program and Department of Medicine, Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tal","family":"Nawy","sequence":"additional","affiliation":[{"name":"Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moritz","family":"Gerstung","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany"},{"name":"Heidelberg University, Faculty of Computer Science and Mathematics, Heidelberg, Germany"},{"name":"Robert Bosch Center for Tumor 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