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

Implicit Tubular Surface Generation Guided by Centerline