DVasMesh: Deep Structured Mesh Reconstruction from Vascular Images for Dynamics Modeling of Vessels

TL;DR

DVasMesh is a deep learning method that automatically generates high-quality vascular meshes from images, significantly reducing manual effort and time, and enabling better vessel dynamics modeling for clinical research.

Contribution

The paper introduces a novel deep learning framework that directly produces structured vascular meshes from images without manual annotation, improving efficiency and accuracy.

Findings

Reduces mesh generation time from 2 hours to 30 seconds.

Achieves high-quality vascular mesh reconstruction on cardiac and cerebral images.

Demonstrates potential for clinical applications in vessel dynamics simulation.

Abstract

Vessel dynamics simulation is vital in studying the relationship between geometry and vascular disease progression. Reliable dynamics simulation relies on high-quality vascular meshes. Most of the existing mesh generation methods highly depend on manual annotation, which is time-consuming and laborious, usually facing challenges such as branch merging and vessel disconnection. This will hinder vessel dynamics simulation, especially for the population study. To address this issue, we propose a deep learning-based method, dubbed as DVasMesh to directly generate structured hexahedral vascular meshes from vascular images. Our contributions are threefold. First, we propose to formally formulate each vertex of the vascular graph by a four-element vector, including coordinates of the centerline point and the radius. Second, a vectorized graph template is employed to guide DVasMesh to estimate the vascular graph. Specifically, we introduce a sampling operator, which samples the extracted features of the vascular image (by a segmentation network) according to the vertices in the template graph. Third, we employ a graph convolution network (GCN) and take the sampled features as nodes to estimate the deformation between vertices of the template graph and target graph, and the deformed graph template is used to build the mesh. Taking advantage of end-to-end learning and discarding direct dependency on annotated labels, our DVasMesh demonstrates outstanding performance in generating structured vascular meshes on cardiac and cerebral vascular images. It shows great potential for clinical applications by reducing mesh generation time from 2 hours (manual) to 30 seconds (automatic).