{
  "abstract": "Purpose: Quantitative susceptibility mapping (QSM) provides venous contrast through the paramagnetic susceptibility of deoxyhemoglobin and can be used to estimate oxygen extraction fraction (OEF), a marker of cerebral metabolism. However, cerebral vein segmentation remains challenging due to artifacts, variability across QSM reconstruction methods, and limited sensitivity of conventional vessel-filtering approaches to small cortical veins. This study proposes a physics- and morphology-constrained deep learning framework for cerebral vein segmentation on QSM. Methods: Thirty subjects were manually segmented and used to train an attention-gated UNet. In addition to supervised Dice and cross-entropy losses, the network incorporated two selfsupervised constraints. A physics-informed loss enforced consistency between the measured local field and a field simulated from predicted veins using dipole convolution. A Frangi vesselness loss encouraged anatomically plausible tubular structures. Segmentation performance was evaluated on an independent multi-center cohort and compared with a prior vessel-filtering method and a supervised U-Net baseline. Physiological relevance was assessed by comparing QSM-derived venous OEF with calibrated fMRI-derived OEF. Results: The proposed model achieved superior segmentation performance (centreline Dice = 0.70 {+/-} 0.08) compared with MSVF (0.44 {+/-} 0.09) and the U-Net baseline (0.57 {+/-} 0.14). Improvements were most pronounced in smaller veins, resulting in greater vessel continuity. Venous OEF derived from the proposed framework demonstrated stronger agreement with calibrated fMRI-derived OEF than the previous approach. Conclusion: Combining supervised learning with physics- and morphology-based constraints improves vein segmentation and enhances the sensitivity of QSM-derived OEF measurements. Code and model weights are publicly available at https://github.com/QuantitativePhysiologyImagingLab/VeinSeg",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medicine, Faculty of Medicine, Universite de Montreal, Montreal, QC, Canada; Department of Physics, Concordia University, Montreal, QC, Canada"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Y. C. Al-Khoury"
    },
    {
      "name": "A. Rezaei"
    },
    {
      "name": "V. Raspa"
    },
    {
      "name": "N. Slanina"
    },
    {
      "name": "H. Gresseau"
    },
    {
      "name": "Y. Xiao"
    },
    {
      "name": "B. P. Thomas"
    },
    {
      "name": "C. J. Gauthier"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/evyf3rvyzersn.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.24.754280",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754280v1.full",
  "id": "evyf3rvyzersn",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754280v1",
  "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",
      "biophysics"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7215038413",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754280v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/evyf3rvyzersn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "10.64898/2026.09.24.754280",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754280v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:biophysics",
      "subfield": "biophysics"
    }
  ],
  "title": "Physics and Morphology Constrained Quantitative Susceptibility Based Segmentation of Cerebral Veins",
  "title_text": "Physics and Morphology Constrained Quantitative Susceptibility Based Segmentation of Cerebral Veins",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/evyf3rvyzersn",
  "venue": "bioRxiv",
  "versions": []
}