Aortic root landmark localization with optimal transport loss for heatmap regression
This work addresses a domain-specific problem for medical imaging in aortic valve surgery, presenting an incremental improvement over prior methods.
The paper tackles aortic root landmark localization to reduce physician burden by automating valve size determination for surgery, achieving significant improvement in estimation error over existing methods on a 3D CT dataset.
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.