Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling

TL;DR

This paper proposes a novel graph convolutional neural network with deep feature fusion for automated intracranial artery labeling, improving accuracy in complex and variable vascular structures.

Contribution

It introduces a stacked graph convolutional architecture with hierarchical feature aggregation for enhanced cerebral artery labeling.

Findings

Outperforms baseline methods with significant margin

Effective in handling complex artery variations

Demonstrates robustness on public datasets

Abstract

Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinical applications and disease diagnoses. Various machine learning algorithms have been carried out for automation in the anatomical labeling of cerebral arteries. However, the task remains challenging because of the high complexity and variations of intracranial arteries. This study investigates a novel graph convolutional neural network with deep feature fusion for cerebral artery labeling. We introduce stacked graph convolutions in an encoder-core-decoder architecture, extracting high-level representations from graph nodes and their neighbors. Furthermore, we efficiently aggregate intermediate features from different hierarchies to enhance the proposed model's representation capability and labeling performance. We perform extensive experiments on public datasets, in which the results prove the superiority of our approach over baselines by a clear margin.