{
  "abstract": "Spatial bioimaging combines raster imagery with vector geometries that encode molecular measurements, connectivity, and anatomical labels. A major challenge is to connect and jointly query these resources at scale, because the vector structures that locate them cannot be searched across resolutions. muDM is a spatial data model for associating vector geometry with its annotated records. Under a single schema, it arranges 2D and 3D geometries, together with their identities and measurements, as a precomputed multi-resolution pyramid, a columnar store that can be queried in place from object storage and equivalent render-ready tiles from the same index. Deployed in a public catalog containing more than 100 datasets, this single model supports connectome meshes, multiplexed tissue imaging, and spatial transcriptomics. For neuron meshes, classifiers which use geometry together with linked synapse counts exceed the performance of geometry-only models, while coarser levels preserved most balanced accuracy and using up to two orders of magnitude less stored geometry.",
  "abstract_shown": true,
  "affiliations": [
    "NovaGen Research Fund"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "B. Ljungquist"
    },
    {
      "name": "N. Hotaling"
    },
    {
      "name": "N. Schaub"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bjb6v2emrwq5x.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.16.759449",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759449v1.full",
  "id": "bjb6v2emrwq5x",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759449v1",
  "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",
      "1304|Cell Image Analysis Techniques"
    ]
  ],
  "openalex": "W7222955483",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759449v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bjb6v2emrwq5x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:31Z",
      "native_id": "10.64898/2026.10.16.759449",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759449v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "muDM: A Queryable Multi-Resolution Data Model for Vector Geometry in Spatial Bioimaging",
  "title_text": "muDM: A Queryable Multi-Resolution Data Model for Vector Geometry in Spatial Bioimaging",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:31Z",
  "url": "https://research.draconitic.com/paper/bjb6v2emrwq5x",
  "venue": "bioRxiv",
  "versions": []
}