用深度学习加测试时优化，自动生成高精度心脏体积网格
Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing
- 先用深度学习快速拟合初始形状，再通过测试时优化精细修正
- 在高曲率区域和部件间距上显著提升准确性，误差更低
- 无需额外标注，可直接用于不同仿真软件的物理模拟
从医学图像生成高质量体积网格是个性化医疗中物理仿真的一大瓶颈。针对复杂医学结构的体积网格化，现有方法常采用基于深度学习的模板变形技术，在测试时实现快速生成并保持高空间精度。然而这些方法在高曲率区域灵活性不足，且部件间距离不真实。本文提出一种简单有效的“抓取-调优”策略，依次结合深度学习与测试时优化，先快速拟合初始形状，再进行样本特异性网格修正。该方法在空间精度和网格质量上均有显著提升，且完全自动化，无需额外训练标签。我们进一步在两种不同软件平台中展示了新生成网格在固体力学仿真中的通用性与实用性。代码已公开于 https://github.com/danpak94/Deep-Cardiac-Volumetric-Mesh。
原文摘要 · Abstract (English)
High-quality volumetric meshing from medical images is a key bottleneck for physics-based simulations in personalized medicine. For volumetric meshing of complex medical structures, recent studies have often utilized deep learning (DL)-based template deformation approaches to enable fast test-time generation with high spatial accuracy. However, these approaches still exhibit limitations, such as limited flexibility at high-curvature areas and unrealistic inter-part distances. In this study, we introduce a simple yet effective snap-and-tune strategy that sequentially applies DL and test-time optimization, which combines fast initial shape fitting with more detailed sample-specific mesh corrections. Our method provides significant improvements in both spatial accuracy and mesh quality, while being fully automated and requiring no additional training labels. Finally, we demonstrate the versatility and usefulness of our newly generated meshes via solid mechanics simulations in two different software platforms. Our code is available at https://github.com/danpak94/Deep-Cardiac-Volumetric-Mesh.
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