论文标题
使用图神经网络和分层精致的自动颅内动脉标记
Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement
论文作者
论文摘要
自动将颅内动脉（ICA）标记具有解剖学名称对特征提取和对颅内血管结构的详细分析有益。由于自然和病理原因，ICA存在显着差异，因此它在自动标签方面具有挑战性。但是，用于评估解剖标签的现有公共数据集有限。我们构建了一个具有729个磁共振血管造影扫描的综合数据集，并提出了图形神经网络（GNN）方法，以通过对属性的关系图中的节点和边缘进行分类来标记动脉。此外，开发了一个分层完善框架，以进一步改善GNN输出，以结合有关ICA的结构和关系知识。我们的方法达到了97.5％的节点标记精度，而Willis节点的所有圆圈都正确标记了63.8％的扫描，并在具有健康和患病的受试者的105次扫描测试中。这是对可用的最新方法的重大改进。自动动脉标记有望最大程度地减少手动努力来表征复杂的ICA网络，并为识别血管疾病的几何危险因素提供了宝贵的信息。我们的代码和数据集可从https://github.com/clatfd/gnn-artlabel获得。
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-ARTLABEL.
