{
  "abstract": "Accurate voxel-level segmentation of pulmonary nodules on chest CT is needed for diameter and volume measurement, growth tracking, and quantitative assessment in lung cancer screening. The task remains difficult: nodules vary in attenuation, size, and contact with vessels or pleura, and clinically relevant morphology often lies in a thin, ambiguous margin. Existing systems commonly trade contour fidelity for speed-large prompt-driven foundation models can be accurate but slow, whereas compact CNN or SSM decoders are efficient yet tend to smooth spiculated and ground-glass borders. We propose HSV-Net (Hybrid State-space-Vision Network), a dual-stream 3D network that is prompt-free after nodule localization: an SSM3D stream provides whole-patch context with linear-complexity state-space blocks, and an OrthoLoRA stream adapts frozen DINOv2 features on axial, coronal, and sagittal slices. A Hybrid Gated Mixer (HGM) fuses the two streams before FPN decoding, and Progressive Contour Learning (PCL) supervises overlap, signed-distance-field geometry, and uncertainty-weighted contour refinement. On a 10-patient QIN Lung CT/QIN-LungCT-Seg development subset (n = 12 tumors; nested patient-level 5-fold CV), HSV-Net obtained the highest mean Dice (90.1% ± 1.0%; 95% CI [88.9, 91.3]) and Boundary F1 (86.3% ± 1.3%) among the compared methods on 128 × 128 × 64 patches, with a latency of 58 ms. A standard full-volume nnU-Net v2 baseline reached 88.6% ± 1.0% Dice on the same QIN folds, still below HSV-Net. On an independent LIDC-IDRI benchmark with expert volumetric consensus masks (186 patients/294 nodules; patient-level 5-fold CV within LIDC, not QIN→LIDC transfer), HSV-Net achieved 87.8% ± 1.1% Dice and 83.6% ± 1.3% Boundary F1, outperforming full-volume nnU-Net (87.1% ± 1.0% Dice). LUNA16 was retained only as a proxy size/localization check with diameter-matched spherical references, not as contour external validation.",
  "abstract_shown": true,
  "affiliations": [
    "Dexing People's Hospital, Dexing, Jiangxi, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Liang Cheng"
    },
    {
      "affs": [
        0
      ],
      "name": "Xinsheng Wan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0z31w4dqntdz7.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1964406",
  "fulltext_url": "https://europepmc.org/articles/PMC13680179",
  "id": "0z31w4dqntdz7",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://europepmc.org/article/MED/42903127",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7220656782",
  "pdf_url": "https://europepmc.org/articles/PMC13680179?pdf=render",
  "pmcid": "PMC13680179",
  "pmid": "42903127",
  "published": "2026-10-06",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/0z31w4dqntdz7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:57Z",
      "native_id": "MED:42903127",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42903127"
    },
    {
      "first_seen": "2026-10-30T12:20:13Z",
      "native_id": "10.3389/fonc.2026.1964406",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1964406/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2234-943X",
      "subfield": "oncology"
    }
  ],
  "title": "HSV-Net: hybrid state-space and self-supervised vision fusion with progressive contour learning for CT pulmonary nodule segmentation.",
  "title_text": "HSV-Net: hybrid state-space and self-supervised vision fusion with progressive contour learning for CT pulmonary nodule segmentation.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:13Z",
  "url": "https://research.draconitic.com/paper/0z31w4dqntdz7",
  "venue": "Frontiers in oncology",
  "versions": []
}