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Related Experiment Video

Updated: Jun 3, 2026

An In Vitro 3D Model and Computational Pipeline to Quantify the Vasculogenic Potential of iPSC-Derived Endothelial Progenitors

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Published on: May 13, 2019

Automated Centerline Extraction From Meshed Vascular Models.

Gala Sanchez Van Moer¹, Shawn C Shadden²

¹Bioengineering, University of California, Berkeley, California, USA.

International Journal for Numerical Methods in Biomedical Engineering

|June 2, 2026

Summary

This summary is machine-generated.

This study introduces an automated framework for extracting vascular centerlines from 3D models. The method accurately identifies vessel pathways, outperforming existing tools, especially for complex geometries.

Keywords:

Eikonal equation centerlines vascular models

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Area of Science:

Biomedical Engineering
Computational Anatomy
Medical Imaging Analysis

Background:

Accurate centerline extraction is crucial for vascular anatomy analysis and biomechanical simulations.
Existing methods like Vascular Modeling Toolkit (VMTK) often require manual intervention (e.g., outlet labeling) and struggle with non-smooth geometries.

Purpose of the Study:

To present an automated framework for extracting centerlines from vascular geometries represented by piecewise linear surface meshes.
To generate accurate and well-centered centerlines efficiently, overcoming limitations of current tools.

Main Methods:

Utilized a finite element method (FEM) approach to solve the Eikonal equation, simulating wave propagation for endpoint identification and centerline tracing.
Implemented the framework on piecewise linear surface meshes, subsequently generating tetrahedral volumetric meshes.

Main Results:

The proposed framework successfully extracted centerlines from 19 diverse vascular meshes.
Demonstrated comparable or superior performance to VMTK, particularly for less smooth surface meshes.
Eliminated the need for tedious manual outlet labeling, reducing processing time and effort.

Conclusions:

The developed framework offers an efficient and automated solution for vascular centerline extraction.
It provides a robust alternative to existing methods, especially for complex and machine learning-generated vascular models.
This advancement facilitates more accurate biomechanical simulations and anatomical studies.