Keywords: Data Processing, Blood vesselsLabeling of intracranial arteries is important for computer-aided diagnosis of cerebrovascular diseases and quantitative analysis of intracranial vasculature. Performance of the previously proposed automated intracranial artery labeling method based on Graph Neural Network (GNN) is limited in datasets with overt cerebrovascular steno-occlusive diseases. In this study, we improved the generalizability of the GNN-based method by using dedicated data augmentation and spatial normalization strategy. The results show that our method is more robust than the previous method in ischemic stroke patients with overt intracranial stenosis or occlusion.
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