论文标题
左心室对2D超声心动图的顶部三腔景观
Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography
论文作者
论文摘要
我们提出了一种新方法，可以在2D超声心动图图像上自动轮廓左心室。与大多数基于预测细分面罩的现有分割方法不同，我们专注于预测该轮廓内（基础点和顶点）中的心内膜轮廓和关键地标点。这提供了一种更接近专家如何执行手动注释的表示，因此产生了在生理上更合理的结果。 我们提出的方法使用基于U-NET体系结构的两头网络。一个头预测了7个轮廓点，另一个头部预测了轮廓的距离图。将这种方法与U-NET和基于点的方法进行了比较，在具有里程碑意义的定位（<4.5mm）和与地面真相轮廓（<3.5mm）的距离方面，达到30 \％的性能增长。
We propose a new method to automatically contour the left ventricle on 2D echocardiographic images. Unlike most existing segmentation methods, which are based on predicting segmentation masks, we focus at predicting the endocardial contour and the key landmark points within this contour (basal points and apex). This provides a representation that is closer to how experts perform manual annotations and hence produce results that are physiologically more plausible. Our proposed method uses a two-headed network based on the U-Net architecture. One head predicts the 7 contour points, and the other head predicts a distance map to the contour. This approach was compared to the U-Net and to a point based approach, achieving performance gains of up to 30\% in terms of landmark localisation (<4.5mm) and distance to the ground truth contour (<3.5mm).
