Accurate extraction of mitral valve shape from clinical tomographic images acquired in patients has proven useful for planning surgical and interventional mitral valve treatments. However, manual extraction of the mitral valve shape is laborious, and the existing automatic extraction methods have not been sufficiently accurate. In this paper, we propose a fully automated method of extracting mitral valve shape from computed tomography (CT) images for the all phases of the cardiac cycle. This method extracts the mitral valve shape based on DenseNet using both the original CT image and the existence probability maps of the mitral valve area inferred by U-Net as input. A total of 1585 CT images from 204 patients with various cardiac diseases including mitral regurgitation (MR) were collected and manually annotated for mitral valve region. The proposed method was trained and evaluated by 10-fold cross validation using the collected data and was compared with the method without the existence probability maps. The mean error of shape extraction error in the proposed method is 0.88 mm, which is an improvement of 0.32 mm compared with the method without the existence probability maps.

翻译：从患者临床断层图像中精确提取二尖瓣形态已被证实对规划二尖瓣手术和介入治疗具有重要价值。然而，手动提取二尖瓣形态费时费力，且现有自动提取方法精度不足。本文提出一种全自动方法，用于从计算机断层扫描（CT）图像中提取心脏周期各阶段的二尖瓣形态。该方法基于DenseNet，以原始CT图像和由U-Net推断的二尖瓣区域存在概率图作为输入，实现二尖瓣形态提取。我们收集了204例包括二尖瓣反流（MR）在内的各类心脏病患者的1585张CT图像，并对二尖瓣区域进行了手动标注。通过10折交叉验证对提出的方法进行训练和评估，并与未使用存在概率图的方法进行比较。本方法的形态提取平均误差为0.88毫米，相较未使用存在概率图的方法提升了0.32毫米。