Graph convolutional networks for automated intracranial artery labeling

Abstract

Purpose: Unruptured intracranial aneurysms (UIAs) can cause aneurysmal subarachnoid hemorrhage, a severe and often lethal type of stroke. Automated labeling of intracranial arteries can facilitate the identification of risk factors associated with UIAs. This study aims to improve intracranial artery labeling using atlas-based features in graph convolutional networks. Approach: We included three-dimensional time-of-flight magnetic resonance angiography scans from 150 individuals. Two widely used graph convolutional operators, GCNConv and GraphConv, were employed in models trained to classify 12 bifurcations of interest. Cross-validation was applied to explore the effectiveness of atlas-based features in node classification. The results were tested for statistically significant differences using a Wilcoxon signed-rank test. Model repeatability and calibration were assessed on the test set for both operators. In addition, we evaluated model interpretability and node feature contribution using explainable artificial intelligence. Results: Atlas-based features led to statistically significant improvements in node classification (p < 0.05). The results showed that the best discrimination and calibration performances were obtained using the GraphConv operator, which yielded a mean recall of 0.87, precision of 0.90, and expected calibration error of 0.02. Conclusions: The addition of atlas-based features improved node classification results. The GraphConv operator, which incorporates higher-order structural information during training, is recommended over the GCNConv operator based on the accuracy and calibration of predicted outcomes.