Aortic root landmark localization with optimal transport loss for heatmap regression

TL;DR

This paper introduces a novel one-step aortic root landmark localization method using optimal transport loss, significantly improving accuracy over existing approaches in 3D CT images for surgical planning.

Contribution

The paper proposes a new heatmap regression approach with optimal transport loss for precise, stable, one-step aortic root landmark localization from coarse 3D images.

Findings

Significant reduction in estimation error compared to existing methods.

Effective application to 3D CT datasets from clinical settings.

Improved learning stability and prediction accuracy.

Abstract

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.