{
  "abstract": "Background/Objectives: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, and accurate tumour segmentation in contrast-enhanced computed tomography is essential for treatment planning, assessment of ablation margins, and post-treatment evaluation. Existing deep learning methods typically use fixed-intensity CT preprocessing, overlooking the adaptive windowing employed by radiologists to enhance tumour visibility. To address this limitation, we propose BCIW-Net, a boundary-conditioned intensity-windowed network that integrates spatially adaptive and differentiable intensity windowing into the segmentation process as a clinically motivated inductive bias. Methods: The proposed framework first generates complementary soft-tissue and tumour-enhancing intensity representations using differentiable sigmoid-based transformations. A primary encoder–decoder module predicts tumour guidance, boundary probabilities, and pixel-wise error proxy maps, which collectively guide the dynamic estimation of spatially varying window centre and width parameters. The resulting adaptively windowed features are selectively blended with fixed-intensity representations through an error-gated mechanism before final refinement in a secondary encoder–decoder network. A multi-task objective jointly optimises tumour segmentation, boundary prediction, error-proxy estimation, and adaptive window regularisation in an end-to-end manner. Results: Experiments on the primary liver cancer (PLC)-CECT dataset demonstrate that BCIW-Net achieved a mean Dice similarity coefficient of 78.68% and an intersection over union (IoU) of 64.85% across five-fold patient-level cross-validation, outperforming the seven evaluated CNN-, Transformer-, and state-space baselines. Evaluation on LiTS2017 without dataset-specific fine-tuning yielded a tumour Dice score of 89.84%, providing preliminary evidence of cross-dataset generalisation. Conclusions: Qualitative feature visualisations show that adaptive windowing enhances tumour representation and boundary delineation, demonstrating the effectiveness and interpretability of BCIW-Net for HCC segmentation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, 43007 Tarragona, Spain",
    "Centre for Cancer Biomarkers & Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK",
    "Department of Pathology and Clinical Bioinformatics, Erasmus MC, 3015 GD Rotterdam, The Netherlands",
    "Queen’s University Belfast, Belfast BT7 1NN, UK",
    "Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tripti Singh"
    },
    {
      "affs": [
        1
      ],
      "name": "Julia Rebecca Kampel"
    },
    {
      "affs": [
        2
      ],
      "name": "Farhan Akram",
      "orcid": "0000-0003-4109-2645"
    },
    {
      "affs": [
        1
      ],
      "name": "Yifan Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Akshat Tiwari"
    },
    {
      "affs": [
        0
      ],
      "name": "Sheetal Santosh Deshpande"
    },
    {
      "affs": [
        3
      ],
      "name": "Shriya Bommaraveni"
    },
    {
      "affs": [
        4
      ],
      "name": "Mohamed Abdel-Nasser"
    },
    {
      "affs": [
        0
      ],
      "name": "Domenec Puig",
      "orcid": "0000-0002-0562-4205"
    },
    {
      "affs": [
        1
      ],
      "name": "Vivek Kumar Singh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1y4b4qk7qcb6a.bib",
  "cited_by": 0,
  "doi": "10.3390/cancers18203317",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13702670/?tool=EBI",
  "id": "1y4b4qk7qcb6a",
  "issns": [
    "2072-6694"
  ],
  "landing_url": "https://www.mdpi.com/2072-6694/18/20/3317",
  "language": "eng",
  "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": [
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7221786727",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13702670/pdf/?tool=EBI",
  "pmcid": "PMC13702670",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/1y4b4qk7qcb6a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:58Z",
      "native_id": "10.3390/cancers18203317",
      "source": "crossref",
      "url": "https://www.mdpi.com/2072-6694/18/20/3317"
    },
    {
      "first_seen": "2026-10-30T12:21:34Z",
      "native_id": "PMC:PMC13702670",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13702670"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2072-6694",
      "subfield": "oncology"
    }
  ],
  "title": "BCIW-Net: Boundary-Conditioned Adaptive CT Windowing for Hepatocellular Carcinoma Segmentation",
  "title_text": "BCIW-Net: Boundary-Conditioned Adaptive CT Windowing for Hepatocellular Carcinoma Segmentation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:21:34Z",
  "url": "https://research.draconitic.com/paper/1y4b4qk7qcb6a",
  "venue": "Cancers",
  "versions": []
}