Implicit Tubular Surface Generation Guided by Centerline
This addresses a specific problem in medical imaging for coronary artery segmentation, enabling easier generation of watertight models without extra stitching steps, but it is incremental as it builds on existing implicit segmentation methods.
The paper tackles the difficulty of generating watertight coronary artery tree models from implicit segmentation results by proposing a method that uses particle interaction and expansion to create uniformly distributed point clouds, followed by incremental Delaunay-based triangulation. The result is a high-quality mesh model with an average error of 0.08 mm, consistent with the implicit surface.
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.