{
  "abstract": "Cell segmentation is a critical yet under-standardized step in spatial transcriptomics, directly affecting cell identity assignment, spatial organization and downstream biological interpretation. Despite rapid advances in spatial transcriptomics technologies, systematic evaluations of segmentation methods across platforms and biological contexts remain limited. Here we benchmark 11 image-based, transcriptomics-based and multimodal segmentation methods across 15 datasets generated by 5 spatial transcriptomics technologies, spanning 12 human and mouse tissue types. We introduce biologically informed evaluation metrics that quantify segmentation quality, transcript recovery, cell composition, morphology, expression purity and spatial organization. Benchmarking reveals substantial variation in performance across datasets, technologies and evaluation criteria, highlighting trade-offs among segmentation completeness, transcriptomic fidelity and biological interpretability. Cross-technology analyses further demonstrate that segmentation choice critically shapes downstream analyses, including cell-type specificity, transcript contamination and ligand–receptor inference. Together, our unified Nextflow pipeline provides a scalable foundation for standardized evaluation of segmentation and method selection in spatial transcriptomics.",
  "abstract_shown": true,
  "affiliations": [
    "Biomedical Sciences Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia",
    "SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence, Thuwal, Saudi Arabia",
    "Algorithmic Dynamic Lab, Department of Oncology and Pathology, Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden",
    "Institute of Chemical Biology, Ilia State University, Tbilisi, Georgia",
    "Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden",
    "Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia",
    "Science for Life Laboratory, Solna, Sweden"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xabier Martínez-de-Morentin",
      "orcid": "0000-0002-8358-9214"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Sumeer A. Khan",
      "orcid": "0000-0002-3345-5904"
    },
    {
      "affs": [
        0
      ],
      "name": "Alberto Maillo"
    },
    {
      "affs": [
        0
      ],
      "name": "Robert Lehmann",
      "orcid": "0000-0001-7071-4226"
    },
    {
      "affs": [
        2
      ],
      "name": "Narsis A. Kiani",
      "orcid": "0000-0002-0949-046X"
    },
    {
      "affs": [
        0,
        1,
        3
      ],
      "name": "Vincenzo Lagani",
      "orcid": "0000-0002-6552-6076"
    },
    {
      "affs": [
        0
      ],
      "name": "David Gomez-Cabrero",
      "orcid": "0000-0003-4186-3788"
    },
    {
      "affs": [
        0,
        1,
        4,
        5,
        6
      ],
      "name": "Jesper Tegner",
      "orcid": "0000-0002-9568-5588"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/02epmnf5pdmyv.bib",
  "cited_by": 3,
  "doi": "10.1038/s41592-026-03272-x",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13736688/",
  "id": "02epmnf5pdmyv",
  "issns": [
    "1548-7091",
    "1548-7105"
  ],
  "landing_url": "https://doi.org/10.1038/s41592-026-03272-x",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Analysis"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7223345698",
  "pdf_url": null,
  "pmcid": "PMC13736688",
  "pmid": "42919952",
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/02epmnf5pdmyv.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:22:05Z",
      "native_id": "PMC13736688",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41592-026-03272-x"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "heading:Analysis",
      "subfield": "analysis-pdes"
    }
  ],
  "title": "Benchmarking segmentation methods for spatial transcriptomics",
  "title_text": "Benchmarking segmentation methods for spatial transcriptomics",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:05Z",
  "url": "https://research.draconitic.com/paper/02epmnf5pdmyv",
  "venue": "Nature Methods",
  "versions": []
}