Automated Centerline Extraction From Meshed Vascular Models.
G, S.V.M. & SC, S. (2026). Automated Centerline Extraction From Meshed Vascular Models.. International journal for numerical methods in biomedical engineering. https://doi.org/10.1002/cnm.70183
G SVM, SC S. Automated Centerline Extraction From Meshed Vascular Models.. International journal for numerical methods in biomedical engineering. 2026; doi: 10.1002/cnm.70183
G SVM, SC S. Automated Centerline Extraction From Meshed Vascular Models.[J]. International journal for numerical methods in biomedical engineering. 2026. DOI: 10.1002/cnm.70183.
@article{g2026,
author = {Sanchez Van Moer G and Shadden SC},
title = {Automated Centerline Extraction From Meshed Vascular Models.},
journal = {International journal for numerical methods in biomedical engineering},
year = {2026},
doi = {10.1002/cnm.70183},
note = {PMID: 42227827},
}
TY - JOUR AU - Sanchez Van Moer G AU - Shadden SC TI - Automated Centerline Extraction From Meshed Vascular Models. T2 - International journal for numerical methods in biomedical engineering PY - 2026 DO - 10.1002/cnm.70183 AN - PMID:42227827 ER -
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.