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

arXiv: https://arxiv.org/abs/2512.03869 · HTML: https://mlsanity.com/paper/2512.03869/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Built on by (most cited first, 1 of 1)

- [Scaling up fine-grained intracranial vessel annotations in computed tomography angiography](https://mlsanity.com/paper/2606.21756/) (2026)

## Abstract (arXiv)

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.