Erin Rainville

VesselSim proposes a two-stage framework for universal 3D blood vessel segmentation: a stochastic, geometry-driven vascular simulation (recursive branching, curvature-controlled growth, collision-aware topology) with domain-randomized intensity synthesis generates 16,500 anatomically plausible 3D angiographic volumes, a 3D U-Net is trained solely on this synthetic data, and a test-time adaptation strategy via a self-supervised mask reconstruction decoder adapts at inference, achieving zero-shot performance competitive with state-of-the-art vascular segmentation foundation models on multiple real datasets spanning MR and CT and several anatomical regions including the brain and kidneys, without real annotated data during training.

VesselSim proposes a two-stage framework for universal 3D blood vessel segmentation: a stochastic, geometry-driven vascular simulation (recursive branching, curvature-controlled growth, collision-aware topology) with domain-randomized intensity synthesis generates 16,500 anatomically plausible 3D angiographic volumes, a 3D U-Net is trained solely on this synthetic data, and a test-time adaptation strategy via a self-supervised mask reconstruction decoder adapts at inference, achieving zero-shot performance competitive with state-of-the-art vascular segmentation foundation models on multiple real datasets spanning MR and CT and several anatomical regions including the brain and kidneys, without real annotated data during training.