VesSynth: Tubes Are All You Need for Robust Cross-Scale Cross-Modal 3D Vessel Segmentation
The cerebral vasculature is central to brain function, with alterations linked to numerous cerebrovascular and neurological disorders. Yet, no single imaging modality can capture the entire cerebral vascular network in humans. Instead, an array of techniques are sensitized to different spatial scales, while trading off resolution for coverage. Magnetic Resonance Imaging (MRI) typically resolves only large pial vessels, while high-resolution microscopy allows micrometer-scale vessels to be mapped over limited spatial extents. These techniques must therefore be combined to obtain a complete mapping of the cerebral angioarchitecture, which underscores the need for automatic, cross-modal vessel segmentation. Here, we introduce VesSynth, a flexible vessel segmentation framework that achieves state-of-the-art accuracy across multiple modalities and spatial resolutions (MR, optical and X-ray imaging), despite being trained entirely on synthetic data. By enabling consistent vascular mapping across scales, this framework paves the way to comprehensive investigation of cerebrovascular organization and its role in health and disease.
Authors
- Ammar Mahmood (ORCID: https://orcid.org/0000-0002-8553-3935)
- Divya Varadarajan (ORCID: https://orcid.org/0000-0002-3517-9739)
- Allison B. McKenzie
- Ara Mahar (ORCID: https://orcid.org/0000-0002-4922-5572)
- Bruce Fischl (ORCID: https://orcid.org/0000-0002-2413-1115)
- E. S. Kirkpatrick
- Jocelyn Mora (ORCID: https://orcid.org/0000-0002-4241-408X)
- Rose Coviello
- Caroline Magnain (ORCID: https://orcid.org/0000-0003-4244-7380)
- Erendira Garcia Pallares (ORCID: https://orcid.org/0000-0003-1524-6698)
- Binh T. Nguyen (ORCID: https://orcid.org/0000-0002-9848-5733)
- Chiara Mauri (ORCID: https://orcid.org/0000-0002-4928-9744)
- Malte Hoffmann (ORCID: https://orcid.org/0000-0002-5511-0739)
- Priyanka Onta
- Aliyah Jama
- Angelina Ream
- Malak Benlahcen
- Etienne Chollet
- Rogeny Herisse
- Zenera Shahu
- Yael Balbastre
- Rahama Sidow
- Annabella Tum
- Jiuver Hernandez
- Vennela Malireddy
- Nkiruka Onumajuru
- Jayvi Hunter
- Cole Analoro
- Maggie Bernier
- Sariya Wood
- Akram Malki
- Itzel Garcia
- Rithika Vigneshwaran
- Isabella Vasi
- Kenton Tran
- Xiangrui Zeng
- Sonia Lakshmanan
- Achraf Malki
- Felicia Bond
- Victoria Chavez
- Michelle Avalos Barbosa
- Nam Tran
- Adam Willis
- Emma Yeon
- Kathryn Rauh
- Hui Wang
- Zahra Ghaouta
- Greisi Sinoballa
- Kaylee Hercules Landaverde
Institutions
- Harvard University (US)
- Massachusetts General Hospital (US)
- Center for Neuro-Oncology (US)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- University College London (GB)
Publication Details
- Journal
- bioRxiv (Cold Spring Harbor Laboratory)
- Published
- 2026-04-06
- DOI
- https://doi.org/10.64898/2026.04.01.715909
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Massachusetts Life Sciences Center
- National Institutes of Health
- National Institute on Aging
- National Institute of Mental Health
- National Institute of Neurological Disorders and Stroke
- National Institute of Biomedical Imaging and Bioengineering