{
  "abstract": "Background: Type-B aortic dissection requires separation of the true and false lumina in heterogeneous, anisotropic CTA. A topology term may reduce fragmentation, but its reproducibility must be tested under a fixed case split and independent native-grid evaluation. Methods: We used the public Graz type-B aortic dissection CTA collection (40 cases; CC BY 4.0) and froze a 28/6/6 case split for training, development, and held-out testing. A compact 2.5-D U-Net trained with cross-entropy plus multiclass Dice was compared with the identical model augmented by a fixed-weight (0.5) differentiable slice-level soft clDice term. Two training seeds were run for each arm. All 40 cases passed the NRRD audit; predictions for six held-out cases were restored to their native grids and scored with case-level 3-D voxel-grid Dice, voxel-grid morphological clDice, 6-connected component counts, physical-volume error, and 2,000-resample case-level paired bootstrap intervals. Results: With seed 20261007, topology-minus-baseline differences were -0.028 (95% CI -0.053 to -0.004) for true-lumen Dice, +0.032 (-0.006 to +0.096) for true-lumen voxel-grid clDice, +0.071 (+0.037 to +0.103) for false-lumen Dice, and +0.073 (+0.048 to +0.103) for false-lumen voxel-grid clDice. With seed 20261008, the corresponding differences were -0.040 (-0.065 to -0.018), +0.013 (-0.034 to +0.071), -0.099 (-0.187 to -0.004), and -0.058 (-0.124 to -0.001). Descriptive pooled means over 12 case rows per arm were true-lumen Dice 0.577 for topology versus 0.611 for baseline and false-lumen Dice 0.409 versus 0.422; these pooled rows are not independent cases. Conclusions: Under this fixed six-case test split, the corrected slice-level topology term did not produce a consistent benefit. True-lumen Dice was lower in both seeds, while false-lumen effects reversed direction between seeds; topology-clDice changes were small and/or interval-crossing. The benchmark supports an auditable failure-analysis workflow but does not support a general topology benefit, clinical use, external generalization, or a panvascular decision system.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Cardiology, Zhongshan Hospital, Fudan University; Institutes of Biomedical Sciences, Fudan University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "L. You"
    },
    {
      "name": "Y. Guo"
    },
    {
      "name": "X. Zhong"
    },
    {
      "name": "Z. Peng"
    },
    {
      "name": "W. Wang"
    },
    {
      "name": "L. Shen"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Ge"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/egzg32xjtv28v.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.07.757473",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757473v1.full",
  "id": "egzg32xjtv28v",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757473v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7222252824",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757473v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/egzg32xjtv28v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:58Z",
      "native_id": "10.64898/2026.10.07.757473",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757473v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "A public-data reproducibility benchmark of a 2.5-D U-Net with a slice-level topology term for type-B aortic dissection segmentation",
  "title_text": "A public-data reproducibility benchmark of a 2.5-D U-Net with a slice-level topology term for type-B aortic dissection segmentation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:58Z",
  "url": "https://research.draconitic.com/paper/egzg32xjtv28v",
  "venue": "bioRxiv",
  "versions": []
}