{
  "abstract": "Automatic extraction and anatomical numbering (R01-R12, L01-L12) of all 24 rib centerlines in thoracic CT is essential for unfolded rib visualization, fracture detection, and surgical navigation, but remains challenging due to adjacent-bone confluence, calcified costal cartilage bridges, kyphotic pose variation, and partial visibility of terminal ribs. We present ribline, an open-source three-stage framework combining: (1) an automatically detected lung-apex and vertebral-midline coordinate frame with a kyphosis-aware azimuthal angle for point ordering along each rib; (2) a hybrid voxel detector fusing an anatomically anchored Semantic Context Forest (SCF) with a lightweight 3D U-Net; and (3) a global stage that decomposes the probability skeleton into candidate curves, assigns rib indices via a bilateral Viterbi dynamic program coupled across both hemithoraces, and refines curves via template recovery and normal-plane centering (RibSnake). On the Siemens Corporate Research 2 mm thoracic CT benchmark (47 training and 12 evaluation scans), our pipeline achieves a labeled Chamfer distance of 2.05 mm, labeled clDice of 0.886 (3 mm) and 0.945 (4 mm), 97.2% labeling accuracy (278/286 ribs), and zero false-positive curves on the evaluation split, and 3.64 mm (median 2.39 mm) with 91.8% labeling accuracy across all 59 annotated scans (1,402 ribs) in 5-fold cross-validation. Code: https://github.com/wq2012/ribline Pretrained Models: https://huggingface.co/collections/wq2012/ribline",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16047",
  "author_total": 1,
  "authors": [
    {
      "name": "Quan Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/enj84pjrv15d4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16047",
  "fulltext_url": null,
  "id": "enj84pjrv15d4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16047",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "eess.IV"
    ],
    [
      "arxiv",
      "q-bio.TO"
    ],
    [
      "openalex",
      "2204|Medical Imaging and Analysis"
    ]
  ],
  "openalex": "W7221757605",
  "pdf_url": "https://arxiv.org/pdf/2610.16047",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/enj84pjrv15d4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:31:01Z",
      "native_id": "2610.16047",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16047"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "arxiv:q-bio.TO",
      "subfield": "physiology"
    }
  ],
  "title": "Hybrid Context Forests and Bilateral Viterbi Matching for 3D Rib Centerline Tracing in CT",
  "title_text": "Hybrid Context Forests and Bilateral Viterbi Matching for 3D Rib Centerline Tracing in CT",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:50Z",
  "url": "https://research.draconitic.com/paper/enj84pjrv15d4",
  "venue": "arXiv",
  "versions": []
}