  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.
