论文标题

CPR-GCN:在自动解剖学标记冠状动脉的有条件部分分离图卷积网络

CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries

论文作者

Yang, Han, Zhen, Xingjian, Chi, Ying, Zhang, Lei, Hua, Xian-Sheng

论文摘要

自动解剖标记在冠状动脉疾病诊断程序中起着至关重要的作用。这个问题的主要挑战是人类解剖学中遗传的大型个人变异性。现有方法通常依赖于位置信息和冠状动脉树拓扑的先验知识,当主要分支机构混淆时,这可能会导致性能不令人满意。在本文中,由于图形神经网络在结构化数据中的广泛应用,我们提出了一个有条件的部分分离图卷积网络(CPR-GCN),该网络同时考虑了位置和CT图像,因为CT图像包含丰富的信息,例如分支大小和跨越方向。 CPR-GCN中包括两个多数部分,分别是部分残基GCN和一个条件提取器。条件提取器是包含3D CNN和LSTM的混合模型,可以提取沿分支的3D空间图像特征。在技​​术方面,部分残基GCN采用分支的位置特征,并以3D空间图像特征为条件,以预测每个分支的标签。在数学方面,我们的方法将部分微分方程(PDE)扭曲成图形建模。从诊所收集了一个具有511名受试者的数据集,并由两个具有两阶段注释过程的专家注释。根据五倍的交叉验证,我们的CPR-GCN产生95.8%的平均值,95.4%的平均值和0.955平均FENEF1,这表现优于最先进的方法。

Automated anatomical labeling plays a vital role in coronary artery disease diagnosing procedure. The main challenge in this problem is the large individual variability inherited in human anatomy. Existing methods usually rely on the position information and the prior knowledge of the topology of the coronary artery tree, which may lead to unsatisfactory performance when the main branches are confusing. Motivated by the wide application of the graph neural network in structured data, in this paper, we propose a conditional partial-residual graph convolutional network (CPR-GCN), which takes both position and CT image into consideration, since CT image contains abundant information such as branch size and spanning direction. Two majority parts, a Partial-Residual GCN and a conditions extractor, are included in CPR-GCN. The conditions extractor is a hybrid model containing the 3D CNN and the LSTM, which can extract 3D spatial image features along the branches. On the technical side, the Partial-Residual GCN takes the position features of the branches, with the 3D spatial image features as conditions, to predict the label for each branches. While on the mathematical side, our approach twists the partial differential equation (PDE) into the graph modeling. A dataset with 511 subjects is collected from the clinic and annotated by two experts with a two-phase annotation process. According to the five-fold cross-validation, our CPR-GCN yields 95.8% meanRecall, 95.4% meanPrecision and 0.955 meanF1, which outperforms state-of-the-art approaches.

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