Implicit Tubular Surface Generation Guided by Centerline

TL;DR

This paper introduces a novel learning-based method for generating watertight coronary artery models from implicit lumen surfaces, using particle interaction and triangulation to produce high-quality meshes with minimal error.

Contribution

The proposed approach enables direct modeling of complex coronary artery shapes as a single piece without stitching, improving accuracy and efficiency over existing methods.

Findings

Achieved an average error of 0.08 mm in mesh generation.

Produced high-quality, watertight coronary artery models.

Eliminated the need for stitching or intersection removal algorithms.

Abstract

Most machine learning-based coronary artery segmentation methods represent the vascular lumen surface in an implicit way by the centerline and the associated lumen radii, which makes the subsequent modeling process to generate a whole piece of watertight coronary artery tree model difficult. To solve this problem, in this paper, we propose a modeling method with the learning-based segmentation results by (1) considering mesh vertices as physical particles and using interaction force model and particle expansion model to generate uniformly distributed point cloud on the implicit lumen surface and; (2) doing incremental Delaunay-based triangulation. Our method has the advantage of being able to consider the complex shape of the coronary artery tree as a whole piece; hence no extra stitching or intersection removal algorithm is needed to generate a watertight model. Experiment results demonstrate that our method is capable of generating high quality mesh model which is highly consistent with the given implicit vascular lumen surface, with an average error of 0.08 mm.