Fully automatic cardiac segmentation can be a fast and reproducible method to extract clinical measurements from an echocardiography examination. The U-Net architecture is the current state-of-the-art deep learning architecture for medical segmentation and can segment cardiac structures in real-time with average errors comparable to inter-observer variability. However, this architecture still generates large outliers that are often anatomically incorrect. This work uses the concept of graph convolutional neural networks that predict the contour points of the structures of interest instead of labeling each pixel. We propose a graph architecture that uses two convolutional rings based on cardiac anatomy and show that this eliminates anatomical incorrect multi-structure segmentations on the publicly available CAMUS dataset. Additionally, this work contributes with an ablation study on the graph convolutional architecture and an evaluation of clinical measurements on the clinical HUNT4 dataset. Finally, we propose to use the inter-model agreement of the U-Net and the graph network as a predictor of both the input and segmentation quality. We show this predictor can detect out-of-distribution and unsuitable input images in real-time. Source code is available online: https://github.com/gillesvntnu/GCN_multistructure

翻译：全自动心脏分割可作为一种快速且可重复的方法，从超声心动图检查中提取临床测量指标。U-Net架构是目前医学分割领域最先进的深度学习架构，能够实时分割心脏结构，其平均误差与观察者间变异相当。然而，该架构仍会产生大量在解剖学上不正确的离群值。本研究利用图卷积神经网络的概念，预测感兴趣结构的轮廓点，而非对每个像素进行标注。我们提出一种基于心脏解剖结构、采用双卷积环的图架构，并在公开的CAMUS数据集上证明该架构能够消除解剖学上不正确的多结构分割结果。此外，本研究贡献包括对图卷积架构的消融实验，以及在临床HUNT4数据集上对临床测量指标的评估。最后，我们提出将U-Net与图网络的模型间一致性作为输入质量和分割质量的预测指标，并证明该预测器能够实时检测分布外及不适宜的输入图像。源代码已在线公开：https://github.com/gillesvntnu/GCN_multistructure