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.

该论文提出了一种基于DenseNet和U-Net的全自动提取计算机断层扫描图像中二尖瓣形状的方法，通过使用原始CT图像和U-Net推断出的二尖瓣存在概率图作为输入来提取二尖瓣形状，并与不使用存在概率图的方法进行比较，使用10倍交叉验证对其进行训练和评估，平均提取误差为0.88毫米，相对于不使用存在概率图的方法，提高了0.32毫米。

利用概率图完全自动化地提取二尖瓣的4D形状