[2605.26277] VesselSim: learning 3D blood vessel segmentation without expert annotations
[Submitted on 25 May 2026]
Title:VesselSim: learning 3D blood vessel segmentation without expert annotations
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Abstract:Blood vessel segmentation is a core task in medical image analysis for the care of vascular diseases and surgical planning, yet the challenges of providing expert vascular annotations pose a major obstacle for the progress of related deep learning techniques. To address this, we propose VesselSim, a two-stage framework for universal 3D blood vessel segmentation that eliminates the need for real annotated data during training. First, we introduce a stochastic, geometry-driven vascular simulation framework that models recursive branching, curvature-controlled growth, and collision-aware topology, followed by domain-randomized intensity synthesis to generate 16,500 anatomically plausible 3D angiographic volumes. Second, a 3D U-Net is trained solely on this synthetic data. To bridge the domain gap from synthetic to real images at inference time, we introduce a test-time adaptation strategy via a self-supervised mask reconstruction decoder, enabling adaptation to unseen clinical scans without prior domain knowledge. We evaluate VesselSim in a zero-shot setting on multiple real-world datasets spanning MR and CT across several anatomical regions, including the brain and kidneys. Despite being trained exclusively on synthetic data, VesselSim achieves performance competitive with state-of-the-art vascular segmentation foundation models. These findings suggest that learning vessel geometry from synthetic tubular structures is effective for robust cross-domain generalization, substantially reducing the reliance on acquired medical imaging data and more importantly, expert annotations.
Comments: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution will be published as part of the MICCAI 2026 proceedings in October
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.26277 [cs.CV]
(or arXiv:2605.26277v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.26277
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Erin Rainville [view email] [v1] Mon, 25 May 2026 19:04:18 UTC (835 KB)
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