{
  "abstract": "Accurate delineation of pancreatic tumors on Magnetic Resonance Imaging (MRI) is important for diagnosis, radiotherapy treatment planning, and outcome assessment, but remains challenging due to complex anatomy and subtle tumor appearance. In routine practice, tumor contours on MRI are produced manually, which is time-consuming and subject to inter-observer variability. Radiotherapy on MRI-Linear Accelerator (MRI-Linac) systems further requires fast and consistent Gross Tumor Volume (GTV) contours for online adaptation, yet most public pancreas tumor segmentation benchmarks focus on Computed Tomography (CT). The Pancreatic Tumor Segmentation in Therapeutic and Diagnostic MRI (PANTHER) challenge addresses this gap by benchmarking automatic pancreatic tumor segmentation on MRI. The dataset includes contrast-enhanced T1-weighted diagnostic MRI and T2-weighted MRI-Linac scans with expert pancreas and tumor annotations, organized into two tasks: (1) tumor segmentation on diagnostic MRI and (2) tumor segmentation on MRI-Linac images. Performance was evaluated using overlap metrics, distance-based metrics, and tumor volume error. The challenge attracted 285 registered participants, with 12 and 9 final submissions for Tasks 1 and 2, respectively. On diagnostic MRI, top methods achieved performance close to inter-reader agreement. Multi-reader analysis suggested that models often reproduced the contouring style of the training annotator, highlighting the importance of annotation quality and consensus. In contrast, performance on MRI-Linac images was lower and more heterogeneous, including cases of complete localization failure.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 24,
  "authors": [
    {
      "name": "Amparo Betancourt Tarifa"
    },
    {
      "name": "Marcel Verheij"
    },
    {
      "name": "René Monshouwer"
    },
    {
      "name": "Hanne Heerkens"
    },
    {
      "name": "Faisal Mahmood"
    },
    {
      "name": "Uffe Bernchou"
    },
    {
      "name": "Emilie Helgesen Karlsson"
    },
    {
      "name": "Ömer Faruk Durugöl"
    },
    {
      "name": "Maximilian Rokuss"
    },
    {
      "name": "Yannick Kirchhoff"
    },
    {
      "name": "Cédric Hémon"
    },
    {
      "name": "Valentin Boussot"
    },
    {
      "name": "Jean-Claude Nunes"
    },
    {
      "name": "Jean-Louis Dillenseger"
    },
    {
      "name": "Chenyuan Bian"
    },
    {
      "name": "Libo Zhang"
    },
    {
      "name": "Yue Ning"
    },
    {
      "name": "Chuanyi Huang"
    },
    {
      "name": "Lisheng Wang"
    },
    {
      "name": "Kyriaki Kolpetinou"
    },
    {
      "name": "George Matsopoulos"
    },
    {
      "name": "John Hermans"
    },
    {
      "name": "Erik van der Bijl"
    },
    {
      "name": "Peter Koopmans"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bx8vwmzyj3qts.bib",
  "cited_by": 0,
  "doi": "10.1016/j.media.2026.104186",
  "fulltext_url": null,
  "id": "bx8vwmzyj3qts",
  "issns": [
    "1361-8415",
    "1361-8423"
  ],
  "landing_url": "https://hal.science/hal-05671365v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.info"
    ],
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "0.spi"
    ],
    [
      "hal",
      "1.info.info-ai"
    ],
    [
      "hal",
      "1.info.info-cv"
    ],
    [
      "hal",
      "1.info.info-im"
    ],
    [
      "hal",
      "1.info.info-mo"
    ],
    [
      "hal",
      "1.sdv.can"
    ],
    [
      "hal",
      "1.sdv.ib"
    ],
    [
      "hal",
      "1.spi.signal"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7165621549",
  "pdf_url": "https://hal.science/hal-05671365/document",
  "pmcid": null,
  "pmid": "42349241",
  "published": "2026-06-23",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/bx8vwmzyj3qts.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:36Z",
      "native_id": "hal-05671365",
      "source": "hal",
      "url": "https://hal.science/hal-05671365v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.sdv.ib",
      "subfield": "systems-synthetic"
    },
    {
      "field": "medicine",
      "origin": "hal:1.sdv.can",
      "subfield": "oncology"
    }
  ],
  "title": "PANTHER Challenge Report: Cross-Domain Pancreatic Tumor Segmentation in Magnetic Resonance Imaging",
  "title_text": "PANTHER Challenge Report: Cross-Domain Pancreatic Tumor Segmentation in Magnetic Resonance Imaging",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:36Z",
  "url": "https://research.draconitic.com/paper/bx8vwmzyj3qts",
  "venue": "Medical Image Analysis",
  "versions": []
}