分层部件生成3D血管模型，突破复杂分支结构建模难题
Hierarchical Part-based Generative Model for Realistic 3D Blood Vessel
- 分三步：先建全局拓扑骨架，再生成局部血管段，最后按骨架组装
- 在真实数据集上优于现有方法，能生成复杂血管网络
- 适合医学影像生成、器官模拟等需要高精度血管建模的场景
3D视觉进展推动了血管建模在医疗中的应用。然而，由于血管具有复杂的分支模式、弯曲形态和不规则形状，准确建模其几何与拓扑结构仍具挑战。本文提出一种分层部件化生成框架，将全局树状拓扑与局部几何细节分离建模。方法分三阶段：(1) 关键图生成以刻画整体层级结构；(2) 基于几何属性生成血管段；(3) 按全局关键图整合局部段完成分层装配。在真实数据集上验证表明，该框架在复杂血管网络建模上优于现有方法。本工作首次成功应用部件化生成范式于3D血管建模，为血管数据生成树立新基准。代码已公开：https://github.com/CybercatChen/PartVessel.git。
原文摘要 · Abstract (English)
Advancements in 3D vision have increased the impact of blood vessel modeling on medical applications. However, accurately representing the complex geometry and topology of blood vessels remains a challenge due to their intricate branching patterns, curvatures, and irregular shapes. In this study, we propose a hierarchical part-based frame work for 3D vessel generation that separates the global binary tree-like topology from local geometric details. Our approach proceeds in three stages: (1) key graph generation to model the overall hierarchical struc ture, (2) vessel segment generation conditioned on geometric properties, and (3) hierarchical vessel assembly by integrating the local segments according to the global key graph. We validate our framework on real world datasets, demonstrating superior performance over existing methods in modeling complex vascular networks. This work marks the first successful application of a part-based generative approach for 3D vessel modeling, setting a new benchmark for vascular data generation. The code is available at: https://github.com/CybercatChen/PartVessel.git.
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