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.

翻译：血管分割是医学图像分析中血管疾病诊疗和手术规划的核心任务，然而专家血管标注的挑战严重阻碍了相关深度学习技术的发展。为此，我们提出VesselSim——一种无需真实标注数据训练、适用于通用三维血管分割的两阶段框架。首先，我们引入基于几何驱动的随机血管仿真框架，建模递归分支、曲率控制生长及碰撞感知拓扑结构，并通过域随机化强度合成生成16，500个解剖合理的三维血管造影体数据。其次，仅基于合成数据训练三维U-Net模型。为解决推理时合成图像与真实图像的域差距，我们引入基于自监督掩膜重建解码器的测试时自适应策略，在无需先验域知识的情况下实现模型对未知临床扫描的适应。我们在涵盖脑部、肾脏等多个解剖区域的MR与CT真实数据集上进行零样本评估。尽管完全基于合成数据训练，VesselSim仍取得与最先进血管分割基础模型相当的竞争力。研究结果表明，从合成管状结构学习血管几何形态可有效实现鲁棒的跨域泛化，显著降低对医学影像采集数据的依赖，特别是专家标注的依赖。