用统一模板重建器官，确保结构连接准确且无伪影。
PrIntMesh: Precise Intersection Surfaces for 3D Organ Mesh Reconstruction
- 基于连通模板联合变形所有子结构，保持内部边界一致
- 在心、海马、肺数据上实现高精度几何重建与拓扑正确性
- 适合临床应用，对少量或噪声数据仍具鲁棒性
人体器官由相互连接的亚结构组成，其几何形状和空间关系彼此制约。然而，大多数深度学习方法将这些部分独立处理，导致解剖上不合理的重建结果。我们提出PrIntMesh，一种基于模板、保持拓扑结构的框架，将器官作为统一系统进行重建。从一个连通模板出发，PrIntMesh联合变形所有子结构以匹配患者特异性解剖，同时显式保留内部边界并强制生成平滑、无伪影的表面。我们在心脏、海马和肺部数据上验证了该方法的有效性，实现了高几何精度、正确的拓扑结构，并在有限或噪声训练数据下仍表现稳健。相比体素和表面基方法，PrIntMesh更优地重建共享界面，维持结构一致性，提供适用于临床的数据高效解决方案。
原文摘要 · Abstract (English)
Human organs are composed of interconnected substructures whose geometry and spatial relationships constrain one another. Yet, most deep-learning approaches treat these parts independently, producing anatomically implausible reconstructions. We introduce PrIntMesh, a template-based, topology-preserving framework that reconstructs organs as unified systems. Starting from a connected template, PrIntMesh jointly deforms all substructures to match patient-specific anatomy, while explicitly preserving internal boundaries and enforcing smooth, artifact-free surfaces. We demonstrate its effectiveness on the heart, hippocampus, and lungs, achieving high geometric accuracy, correct topology, and robust performance even with limited or noisy training data. Compared to voxel- and surface-based methods, PrIntMesh better reconstructs shared interfaces, maintains structural consistency, and provides a data-efficient solution suitable for clinical use.
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