中心线引导的隐式管状曲面生成
Implicit Tubular Surface Generation Guided by Centerline
摘要：
大多数基于机器学习的冠状动脉分割方法通过中心线和相关的管腔半径以隐式方式表示血管管腔表面，这使得后续的建模过程难以生成完整的水密冠状动脉树模型。针对这一问题，本文提出了一种基于学习分割结果的建模方法：（1）将网格顶点视为物理粒子，利用相互作用力模型和粒子膨胀模型在隐式管腔表面生成均匀分布的点云;（2）进行增量Delaunay三角剖分。我们的方法具有能够将冠状动脉树的复杂形状作为一个整体考虑的优点;因此不需要额外的拼接或相交去除算法来生成水密模型。实验结果表明，该方法能够生成与给定隐式血管腔表面高度一致的高质量网格模型，平均误差为0.08 mm。
英文摘要：
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.
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