{
  "abstract": "Ductular reactions (DRs) are dynamic and complex multicellular responses that occur as a result of various hepatic injuries. Precise identification and quantification of the extent of DRs is a cornerstone of pre‐clinical modelling of liver disease, with links to inflammation, fibrosis, regeneration, and disease severity. Here, we apply a deep learning model, Deep Understanding Convolutional Kernel (DUCK‐Net), to the automated detection and segmentation of DRs in whole‐slide histopathological images of murine models of liver damage. Following annotation of a training dataset by a specialist liver histopathologist, we demonstrate accelerated performance and accurate detection, achieving a mean Dice coefficient (model‐expert segmentation overlap) of 85.4% and a specificity of 98%, indicating minimal false positives. Evaluation of model validity and utility was achieved with a histological time course of cholestatic injury and recovery using 3,5‐diethoxycarbonyl‐1,4‐dihydrocollidine diet (DDC) in mice. When assessed against a multiple linear regression model incorporating core epithelial and stromal components of the DR as quantified using immunohistochemistry (IHC), DUCK‐Net predicted the spatiotemporal response to injury and repair/resolution with a coefficient of determination (R 2) of 0.88. Moreover, DUCK‐Net kinetics strongly correlated with published spatial transcriptomic (Stereo‐seq) analysis of the DDC model, demonstrating that H&E‐based segmentation captured molecular DR dynamics comparable to or exceeding that of individual IHC markers without the need for immunostaining. DUCK‐Net provides a novel and accessible platform for rapid, accurate histological quantification of liver injury reflective of the matrix‐rich, multicellular regenerative niche observed in DRs. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Genetics and Cancer University of Edinburgh Edinburgh UK",
    "Department of Oncology University of Oxford Oxford UK",
    "Institute of Regeneration and Repair University of Edinburgh Edinburgh UK",
    "Department of Clinical Surgery University of Edinburgh Edinburgh UK",
    "Centre for Medical Informatics, Usher Institute University of Edinburgh Edinburgh UK",
    "Edinburgh Pathology University of Edinburgh Edinburgh UK"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 16,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nathalie Feeley",
      "orcid": "0000-0003-0124-6398"
    },
    {
      "affs": [
        0
      ],
      "name": "Kai Williams"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Caitlin McCaffrey"
    },
    {
      "affs": [
        0
      ],
      "name": "Daniel Field",
      "orcid": "0000-0001-7505-6584"
    },
    {
      "affs": [
        0
      ],
      "name": "Kyle Davies"
    },
    {
      "affs": [
        0
      ],
      "name": "Hugh Warden"
    },
    {
      "affs": [
        0
      ],
      "name": "Luke Boulter"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Steve Thorn",
      "orcid": "0000-0002-9962-9356"
    },
    {
      "affs": [
        2
      ],
      "name": "Tak Yung Man"
    },
    {
      "affs": [
        2
      ],
      "name": "Wei‐Yu Lu",
      "orcid": "0000-0002-0419-0257"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Stephen J Wigmore",
      "orcid": "0000-0002-3614-8002"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Ewen M Harrison"
    },
    {
      "affs": [
        2
      ],
      "name": "Stuart J Forbes"
    },
    {
      "affs": [
        1
      ],
      "name": "Ian P Tomlinson"
    },
    {
      "affs": [
        2,
        5
      ],
      "name": "Timothy J Kendall",
      "orcid": "0000-0002-4174-2786"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Rachel V Guest",
      "orcid": "0000-0003-3213-7688"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/84sregd6gjhcy.bib",
  "cited_by": 0,
  "doi": "10.1002/path.70113",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13728815/",
  "id": "84sregd6gjhcy",
  "issns": [
    "0022-3417",
    "1096-9896"
  ],
  "landing_url": "https://doi.org/10.1002/path.70113",
  "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": [
    [
      "heading",
      "Original Article"
    ],
    [
      "openalex",
      "1304|Cell Image Analysis Techniques"
    ]
  ],
  "openalex": "W7211963892",
  "pdf_url": null,
  "pmcid": "PMC13728815",
  "pmid": "42711623",
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/84sregd6gjhcy.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:21:43Z",
      "native_id": "PMC13728815",
      "source": "pmc",
      "url": "https://doi.org/10.1002/path.70113"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1304|Cell Image Analysis Techniques",
      "subfield": "biophysics"
    }
  ],
  "title": "DUCK‐Net: automated deep learning segmentation of ductular reactions in murine liver injury captures multicellular niche dynamics from H&E morphology",
  "title_text": "DUCK‐Net: automated deep learning segmentation of ductular reactions in murine liver injury captures multicellular niche dynamics from H&E morphology",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:21:43Z",
  "url": "https://research.draconitic.com/paper/84sregd6gjhcy",
  "venue": "The Journal of Pathology",
  "versions": []
}