Automated Centerline Extraction From Meshed Vascular Models

IF 2.1 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Gala Sanchez Van Moer, Shawn C. Shadden
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引用次数: 0

Abstract

Centerlines of vascular structures are essential for the analysis of vascular anatomy as well as the development of computational models for simulating biomechanics. In this paper, we present a centerline extraction framework for vascular geometries implemented on piece-wise linear surface meshes, from which tetrahedral volumetric meshes are generated. Our approach involves solving the Eikonal equation using the finite element method to describe wave propagation for automatic endpoint identification and centerline tracing. We evaluated the framework on 19 vascular meshes with varying anatomies and construction methods, comparing our results to those obtained using the widely used Vascular Modeling Toolkit (VMTK) as a benchmark. The proposed method produces well-centered centerlines within a reasonable time frame without the need for tedious outlet labeling required by VMTK. Furthermore, it considerably outperforms VMTK when extracting centerlines from less smooth surface meshes, such as those generated using machine learning-based segmentation techniques.

Abstract Image

网格化血管模型的自动中心线提取。
血管结构的中心线对于血管解剖分析以及模拟生物力学的计算模型的发展是必不可少的。在本文中,我们提出了一种血管几何的中心线提取框架,该框架实现在分段线性表面网格上,从中生成四面体体积网格。我们的方法包括用有限元法求解Eikonal方程来描述波的传播,用于自动端点识别和中心线跟踪。我们在19个不同解剖结构和构建方法的血管网格上评估了该框架,并将我们的结果与使用广泛使用的血管建模工具包(VMTK)作为基准的结果进行了比较。所提出的方法在合理的时间范围内产生中心位置良好的中心线,而不需要VMTK所需的繁琐的出口标记。此外,当从不太光滑的表面网格中提取中心线时,例如使用基于机器学习的分割技术生成的中心线,它大大优于VMTK。
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来源期刊
International Journal for Numerical Methods in Biomedical Engineering
International Journal for Numerical Methods in Biomedical Engineering ENGINEERING, BIOMEDICAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
4.50
自引率
9.50%
发文量
103
审稿时长
3 months
期刊介绍: All differential equation based models for biomedical applications and their novel solutions (using either established numerical methods such as finite difference, finite element and finite volume methods or new numerical methods) are within the scope of this journal. Manuscripts with experimental and analytical themes are also welcome if a component of the paper deals with numerical methods. Special cases that may not involve differential equations such as image processing, meshing and artificial intelligence are within the scope. Any research that is broadly linked to the wellbeing of the human body, either directly or indirectly, is also within the scope of this journal.
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