Abstract
Automatically labeling intracranial arteries (ICA) with their anatomical names is beneficial for feature extraction and detailed analysis of intracranial vascular structures. There are significant variations in the ICA due to natural and pathological causes, making it challenging for automated labeling. However, the existing public dataset for evaluation of anatomical labeling is limited. We construct a comprehensive dataset with 729 Magnetic Resonance Angiography scans and propose a Graph Neural Network (GNN) method to label arteries by classifying types of nodes and edges in an attributed relational graph. In addition, a hierarchical refinement framework is developed for further improving the GNN outputs to incorporate structural and relational knowledge about the ICA. Our method achieved a node labeling accuracy of 97.5%, and 63.8% of scans were correctly labeled for all Circle of Willis nodes, on a testing set of 105 scans with both healthy and diseased subjects. This is a significant improvement over available state-of-the-art methods. Automatic artery labeling is promising to minimize manual effort in characterizing the complicated ICA networks and provides valuable information for the identification of geometric risk factors of vascular disease. Our code and dataset are available at https://github.com/clatfd/GNN-ART-LABEL.
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Acknowledgements
This work was supported by National Institute of Health under grant R01-NS092207. We are grateful for the collaborators who provided the datasets for this study, including the CROP and BRAVE investigators, and researchers from the University of Arizona, USA, Beijing Anzhen hospital, China, and Tsinghua University, China and the public data from The University of North Carolina at Chapel Hill (distributed by the MIDAS Data Server at Kitware Inc.). We acknowledge NIVIDIA for providing the GPU used for training the neural network model.
Our code and dataset are available at https://github.com/clatfd/GNN-ART-LABEL.
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Chen, L., Hatsukami, T., Hwang, JN., Yuan, C. (2020). Automated Intracranial Artery Labeling Using a Graph Neural Network and Hierarchical Refinement. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_8
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