# Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement

arXiv: https://arxiv.org/abs/2007.14472 · HTML: https://mlsanity.com/paper/2007.14472/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2020.

## Built on by (most cited first, 11 of 11)

- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography](https://mlsanity.com/paper/2312.17670/) (2023)
- [NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2305.15911/) (2023)
- [Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling](https://mlsanity.com/paper/2205.10757/) (2022)
- [Leptomeningeal Collateral Detection on DSA via Vessel-Graph Neural Networks](https://mlsanity.com/paper/2606.14828/) (2026)
- [Deep Open Snake Tracker for Vessel Tracing](https://mlsanity.com/paper/2107.09049/) (2021)
- [ArteryX: A Reliable End-to-End Toolbox for Standardized Intracranial Artery Feature Extraction from 3D TOF-MRA](https://mlsanity.com/paper/2507.07920/) (2025)
- [Depth-Sequence Transformer (DST) for Segment-Specific ICA Calcification Mapping on Non-Contrast CT](https://mlsanity.com/paper/2507.08214/) (2025)
- [Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning](https://mlsanity.com/paper/2509.17726/) (2025)
- [Anatomically Conditioned Recurrent Refinement for Topology-Aware Circle of Willis Segmentation](https://mlsanity.com/paper/2606.12319/) (2026)

## Abstract (arXiv)

Automatically labeling intracranial arteries (ICA) with their anatomical names is beneficial for feature extraction and detailed analysis of intracranial vascular structures. There are significant variations in the ICA due to natural and pathological causes, making it challenging for automated labeling. However, the existing public dataset for evaluation of anatomical labeling is limited. We construct a comprehensive dataset with 729 Magnetic Resonance Angiography scans and propose a Graph Neural Network (GNN) method to label arteries by classifying types of nodes and edges in an attributed relational graph. In addition, a hierarchical refinement framework is developed for further improving the GNN outputs to incorporate structural and relational knowledge about the ICA. Our method achieved a node labeling accuracy of 97.5%, and 63.8% of scans were correctly labeled for all Circle of Willis nodes, on a testing set of 105 scans with both healthy and diseased subjects. This is a significant improvement over available state-of-the-art methods. Automatic artery labeling is promising to minimize manual effort in characterizing the complicated ICA networks and provides valuable information for the identification of geometric risk factors of vascular disease. Our code and dataset are available at https://github.com/clatfd/GNN-ARTLABEL.