{
  "abstract": "Large-scale phenotyping of the thoracic aorta requires a workflow in which every step, from segmentation to phenotype extraction, runs without manual intervention. We present a robust and scalable framework that converts contrast-enhanced CT into anatomical landmarks, a patient-specific quadrilateral mesh, a wall-stress field and regional phenotypes of the thoracic aorta. Four nnU-Net segmentation tasks delineate the aorta, its anatomical regions and two partitions of the aortic root. A surface-curve-point procedure derives the annulus, the sinotubular junction, the ostia of the supra-aortic branches, the cusp nadirs and the commissures from the interfaces between segmented regions. We fit a structured template of 24,882 quadrilateral elements to the segmented aortic surface by staged, differentiable mesh deformation and compute the transmural mean wall stress with PyTorch-FEA. The structured mesh provides node-to-node correspondence across patients, anchored to the anatomy by the landmarks, from which the framework extracts regional morphological and biomechanical phenotypes.",
  "abstract_shown": true,
  "affiliations": [
    "University of Miami"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "J. Chen"
    },
    {
      "name": "L. Qian"
    },
    {
      "name": "R. Gong"
    },
    {
      "name": "C. Sun"
    },
    {
      "name": "T. Pham"
    },
    {
      "name": "C. Martin"
    },
    {
      "name": "M. Zafar"
    },
    {
      "name": "J. Elefteriades"
    },
    {
      "name": "W. Sun"
    },
    {
      "affs": [
        0
      ],
      "name": "L. Liang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4ktd66v8k9fkb.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.11.758402",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.11.758402v1.full",
  "id": "4ktd66v8k9fkb",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.11.758402v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioengineering"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7221682091",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.11.758402v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4ktd66v8k9fkb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:35Z",
      "native_id": "10.64898/2026.10.11.758402",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.11.758402v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioengineering",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "A robust and scalable image analysis framework for phenotyping human thoracic aorta",
  "title_text": "A robust and scalable image analysis framework for phenotyping human thoracic aorta",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:35Z",
  "url": "https://research.draconitic.com/paper/4ktd66v8k9fkb",
  "venue": "bioRxiv",
  "versions": []
}