Anatomical landmark localization is gaining attention to ease the burden on physicians. Focusing on aortic root landmark localization, the three hinge points of the aortic valve can reduce the burden by automatically determining the valve size required for transcatheter aortic valve implantation surgery. Existing methods for landmark prediction of the aortic root mainly use time-consuming two-step estimation methods. We propose a highly accurate one-step landmark localization method from even coarse images. The proposed method uses an optimal transport loss to break the trade-off between prediction precision and learning stability in conventional heatmap regression methods. We apply the proposed method to the 3D CT image dataset collected at Sendai Kousei Hospital and show that it significantly improves the estimation error over existing methods and other loss functions. Our code is available on GitHub.

翻译：解剖标志点定位技术正日益受到关注，以减轻医师的工作负担。针对主动脉根部标志点定位，主动脉瓣的三个铰合点可通过自动确定经导管主动脉瓣植入手术所需的瓣膜尺寸来减轻负担。现有的主动脉根部标志点预测方法主要采用耗时的两步估计方法。我们提出了一种即使从粗糙图像中也能实现高精度的一步式标志点定位方法。该方法采用最优传输损失来突破传统热图回归方法中预测精度与学习稳定性之间的权衡。我们将所提方法应用于仙台厚生医院收集的3D CT图像数据集，结果表明其估计误差较现有方法及其他损失函数均有显著改善。我们的代码已在GitHub上公开。