ArteryX: A Reliable End-To-End Toolbox for Standardized Intracranial Artery Feature Extraction From 3D TOF-MRA.
Authors
Affiliations (5)
Affiliations (5)
- Department of Neurology, University of Rochester, Rochester, New York, USA.
- Department of Physics, University of Rochester, Rochester, New York, USA.
- Department of Imaging Sciences, University of Rochester, Rochester, New York, USA.
- Department of Electrical & Computer Engineering, University of Rochester, Rochester, New York, USA.
- Department of Biomedical Engineering, University of Rochester, Rochester, New York, USA.
Abstract
Cerebrovascular research heavily relies on quantitative analysis of intracranial arteries from time-of-flight magnetic resonance angiography (TOF-MRA), yet existing processing pipelines remain limited by inconsistent artery labeling and a high manual correction burden. We present ArteryX, a toolbox for extracting artery features that standardizes artery classification across proximal and distal vascular territories. ArteryX integrates segmentation handling, isotropic geometric processing, vessel-fused graph construction, and constrained landmark-based classification within a unified artery-specific feature reporting and reproducible workflow. The toolbox extracts artery-level morphological, topological, and complexity features including total length, mean radius, volume, surface area, branch count, tortuosity, and fractal dimensionality for standardized artery segments. Test and validation were performed using two complementary datasets: (1) publicly available TopBrain-Challenge benchmarking with annotated arteries, (2) synthetic known-reference validation. Another dataset with and without cerebral small vessel disease (CSVD) was used for exploratory in vivo analysis. In TopBrain data analyses, ArteryX with supervised nnUnet segmentation showed minimal bias, while iCafe showed larger bias and a large limit of agreement. ArteryX demonstrated robust downstream quantification performance across segmentation sources (unsupervised/supervised). Agreement analyses showed minimal bias for radius and good sensitivity of extent-dependent metrics throughout the noisier segmentations compared to the state-of-the-art iCafe toolbox. Furthermore, a stage-wise human-in-the-loop protocol required less manual intervention than iCafe in the reported setup. In an exploratory in vivo cohort (48 CSVD+, 20 CSVD-), ArteryX-derived distal and territory-level features showed group-level differences that were not observed with iCafe. To facilitate adoption and reproducibility, ArteryX is designed as a community-oriented toolbox with versioned builds, tutorials, and documentation.