We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are extracted using convolutional neural networks and enriched using graph neural networks. Our graph neural network is structure-aware by having each node aggregate information from its local neighbors and position-aware by encoding node positions in the graph.

中文翻译：

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用于气道标记的结构和位置感知图神经网络


我们提出了一种新颖的基于图的方法来标记给定气道树分割的解剖分支。该方法将气道标记制定为气道树图中的分支分类问题，其中使用卷积神经网络提取分支特征并使用图神经网络丰富分支特征。我们的图神经网络通过让每个节点聚合来自其本地邻居的信息来实现结构感知，并通过对图中的节点位置进行编码来实现位置感知。