Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing

TL;DR

This paper presents a novel snap-and-tune approach that combines deep learning and test-time optimization to produce high-fidelity, accurate, and realistic cardiovascular volumetric meshes from medical images, enhancing simulation readiness.

Contribution

It introduces a simple, automated method that improves mesh quality and accuracy without additional training labels, outperforming existing DL-based template deformation techniques.

Findings

Significant improvements in spatial accuracy and mesh quality.

Method is fully automated and requires no extra training labels.

Meshes are validated via solid mechanics simulations.

Abstract

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.