Improving automated intracranial artery labeling using atlas-based features in graph convolutional nets

Automated labeling of intracranial arteries facilitates the identification of risk predictors for aneurysm development, as well as the detection of aneurysms. Although such methods have been previously developed, accurate labeling is challenged by the large variation found in the configuration of the circulatory anastomosis of intracranial arteries, named the Circle of Willis (CoW). Recent studies have shown the potential of deep learning techniques with geometric features to handle large topological variation. We propose a method to incorporate node features based on an atlas in a graph convolutional net (GCN). Time-of-flight magnetic resonance angiography (MRAs) images without intracranial aneurysms were divided in a training set (N=32) and test set (N=16). The atlas was used to identify the coordinates of eleven main CoW artery bifurcations. Node features were obtained by computing the reciprocal Euclidean distances between these coordinates and each node position. Results showed statistically significant improvements of node classification using the atlas compared to other commonly used node features (p<0.005, Wilcoxon). We achieved an average recall of 0.84, precision of 0.71, and F1-score of 0.77 for all CoW nodes, with 4.1 ± 1.9 falsely classified nodes per image. The results indicate that atlas-based features boost the ability of a GCN to handle anatomical variants and smaller arteries. The performance of the model could be further improved by including additional MRAs. In addition, the model should be tested on MRAs from other institutions to assess reproducibility and generalizability.