论文标题
气道标签的结构和位置感知图神经网络
Structure and position-aware graph neural network for airway labeling
论文作者
论文摘要
我们提出了一种基于图形的新方法，用于标记给定气道树分割的解剖分支。所提出的方法将气道标记作为Airway树图中的分支分类问题，其中分支特征是使用卷积神经网络（CNN）提取的，并使用图神经网络富集。我们的图神经网络是通过从其本地邻居中获得每个节点聚合信息的结构感知，并通过编码图中的节点位置来感知位置。 我们评估了来自慢性阻塞性肺部疾病（COPD）的受试者的220个气道树的拟议方法。结果表明，与基线方法相比，我们的方法是计算上有效的，并且可以显着提高分支分类的性能。我们的方法的总体平均准确性达到91.18 \％，标记所有18个分段气道分支，而标准CNN方法获得的83.83 \％。我们在https://github.com/diagnijmegen/spgnn上发布了源代码。所提出的算法也可在https://grand-challenge.org/algorithms/airway-anatomical-labeling/上公开获得。
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 (CNN) 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. We evaluated the proposed method on 220 airway trees from subjects with various severity stages of Chronic Obstructive Pulmonary Disease (COPD). The results demonstrate that our approach is computationally efficient and significantly improves branch classification performance than the baseline method. The overall average accuracy of our method reaches 91.18\% for labeling all 18 segmental airway branches, compared to 83.83\% obtained by the standard CNN method. We published our source code at https://github.com/DIAGNijmegen/spgnn. The proposed algorithm is also publicly available at https://grand-challenge.org/algorithms/airway-anatomical-labeling/.
