论文标题
轻便时空图表，用于分割和射血分数预测的心脏超声预测
Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound
论文作者
论文摘要
超声心动图参数的准确和一致的预测对于心血管诊断和治疗很重要。特别是，左心室的分割可用于得出心室体积，射血分数（EF）和其他相关测量值。在本文中，我们提出了一种新的自动化方法，称为地位谱图，用于通过检测解剖关键来预测射血分数并分割左心室。基于图形卷积网络（GCN）的直接坐标回归模型用于检测关键点。 GCN可以根据每个关键点的局部外观以及所有关键点的全局空间和时间结构来学习心脏形状。我们在echonet基准数据集上评估了我们的电子位学模型。与语义分割相比，GCN显示出准确的分割和鲁棒性和推理运行时的改进。 EF是同时计算的，与分割同时计算，我们的方法还获得了最新的射血分数估计。源代码可在线获得：https：//github.com/guybenyosef/echographs。
Accurate and consistent predictions of echocardiography parameters are important for cardiovascular diagnosis and treatment. In particular, segmentations of the left ventricle can be used to derive ventricular volume, ejection fraction (EF) and other relevant measurements. In this paper we propose a new automated method called EchoGraphs for predicting ejection fraction and segmenting the left ventricle by detecting anatomical keypoints. Models for direct coordinate regression based on Graph Convolutional Networks (GCNs) are used to detect the keypoints. GCNs can learn to represent the cardiac shape based on local appearance of each keypoint, as well as global spatial and temporal structures of all keypoints combined. We evaluate our EchoGraphs model on the EchoNet benchmark dataset. Compared to semantic segmentation, GCNs show accurate segmentation and improvements in robustness and inference runtime. EF is computed simultaneously to segmentations and our method also obtains state-of-the-art ejection fraction estimation. Source code is available online: https://github.com/guybenyosef/EchoGraphs.
