An Automated Framework for Large-Scale Graph-Based Cerebrovascular Analysis

2025-12-03

AI Summary

This study addresses the challenge of automating and scaling quantitative morphological analysis of cerebral vasculature to enable population-level investigations of vascular health and aging. We propose the first fully automated, multi-scale cerebral vascular analysis framework: it employs atlas-guided centerline extraction and skeletonization to generate topologically consistent vascular graph representations; subsequently, it systematically computes 15 quantitative features—spanning morphological, topological, fractal, and geometric domains—across hierarchical scales from whole-brain networks to arterial subregions. Validated on 570 high-quality cerebral vascular datasets, the features demonstrate excellent test–retest reliability (ICC > 0.9) and robustly capture age-, sex-, and education-associated gradients in vascular complexity. The framework establishes a reproducible, standardized computational foundation for large-scale cerebrovascular phenomics studies.

Technology Category

• Computer Vision: Segmentation
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Feature Construction/Reformulation

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Web data visualization
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

We present CaravelMetrics, a computational framework for automated cerebrovascular analysis that models vessel morphology through skeletonization-derived graph representations. The framework integrates atlas-based regional parcellation, centerline extraction, and graph construction to compute fifteen morphometric, topological, fractal, and geometric features. The features can be estimated globally from the complete vascular network or regionally within arterial territories, enabling multiscale characterization of cerebrovascular organization. Applied to 570 3D TOF-MRA scans from the IXI dataset (ages 20-86), CaravelMetrics yields reproducible vessel graphs capturing age- and sex-related variations and education-associated increases in vascular complexity, consistent with findings reported in the literature. The framework provides a scalable and fully automated approach for quantitative cerebrovascular feature extraction, supporting normative modeling and population-level studies of vascular health and aging.

Problem

• Automates large-scale graph-based analysis of cerebrovascular networks
• Extracts multiscale morphometric and topological features from vessel graphs
• Enables population studies of vascular aging and complexity variations

Innovation

• Automated skeletonization-derived graph modeling of vessels
• Multiscale feature extraction from global and regional networks
• Scalable framework for population-level vascular analysis