ARIG-GCN: Anatomical Relationship and Isomorphic Graph Approximation Guided Graph Convolutional Network for Automated ASPECTS Scoring on Non-Contrast CT

Abstract

The Alberta Stroke Program Early CT Score (AS-PECTS) is a systematic method for assessing the extent of early ischemic changes on non-contrast CT (NCCT) of patients with acute ischemic stroke (AIS). The ASPECTS regions are anatomically and physiologically interconnected, making them suitable for analysis by graph neural networks. However, most existing methods fail to effectively use the relationships and bilateral differences. This study designs an Anatomical Relationship and Isomorphic Graph approximation guided Graph Convolutional Network for ASPECTS scoring on NCCT. Firstly, we propose a node construction guided by anatomical structures, i.e., utilizing the region anatomical relationship adjacency matrix of the ASPECTS regions to build the nodes. Secondly, to optimize the information propagation among nodes, we propose isomorphic graph approximation, utilizing edge learning, connectivity-based subgraph selection, and supervised isomorphic subgraph approximation to supervise the construction of isomorphic subgraphs. We validate our method on private AIS datasets which included NCCT scans of 257 AIS patients. The results show that the proposed method achieves interclass correlation coefficients of 0.8554 for total ASPECTS, and accuracy of 90.61% for dichotomized ASPECTS scoring (<=4 vs. >4), outperforming 9 state-of-the-art methods.