{
  "abstract": "Forensic assessments of postmortem decomposition often rely on external, whole-body signs, yet structural changes within organs remain largely unquantified. Using whole-body computed tomography scans of 59 decedents from the New Mexico Decedent Image Database, we conduct an organ-level statistical shape analysis of decomposing brains and kidneys, representing each organ as a three-dimensional point cloud of its outer surface and internal cavity boundaries. Decomposition creates voids, fragmentation, and structural heterogeneity, complicating point correspondence. Registration-based principal component analysis and principal nested cones yield interpretable orderings for brains only. We therefore construct a registration-free pipeline using persistent homology and a fusion of complementary topological distances selected through simulations of cavity formation and growth. Clustering identifies broad patterns of internal decay, and seriation produces organ-specific decomposition atlases. The brain atlas combines geometric and topological rankings; the kidney atlas uses topology alone. Rank agreement is higher between paired kidneys (Kendall's $τ= 0.59$) than between brains and kidneys ($τ= 0.34$ to $0.39$), indicating asynchronous decompositions within a body. Whole-body stages agree more closely with brain than with kidney rankings. These findings support quantitative forensic taphonomy by complementing whole-body staging with organ-level, interior-based measurements. We release the organ point clouds and analysis code to support research on organ decomposition and shape analysis without reliable correspondence.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.00278",
  "author_total": 5,
  "authors": [
    {
      "name": "Yanyan Zhan"
    },
    {
      "name": "Yuexuan Wu"
    },
    {
      "name": "Ian L. Dryden"
    },
    {
      "name": "Zeloso M. Tiago"
    },
    {
      "name": "Edward C. Schwalbe"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/azjkhbrxet0e3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.00278",
  "fulltext_url": null,
  "id": "azjkhbrxet0e3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.00278",
  "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",
      "stat.AP"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.00278",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/azjkhbrxet0e3.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:21Z",
      "native_id": "2611.00278",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.00278"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Decomposition Atlases of Brains and Kidneys from Postmortem CT: Shape and Topological Data Analysis",
  "title_text": "Decomposition Atlases of Brains and Kidneys from Postmortem CT: Shape and Topological Data Analysis",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/azjkhbrxet0e3",
  "venue": "arXiv",
  "versions": []
}