Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography

TL;DR

This paper introduces a novel deep learning method for automatically contouring the left ventricle in 2D echocardiography by predicting key landmarks and contours, aligning more closely with expert manual annotations.

Contribution

A two-headed U-Net based network that predicts both contour points and distance maps, improving landmark localization and contour accuracy over existing methods.

Findings

Achieved up to 30% performance improvement in landmark localization.

Predicted landmarks within 4.5mm of ground truth.

Contour distance errors less than 3.5mm.

Abstract

We propose a new method to automatically contour the left ventricle on 2D echocardiographic images. Unlike most existing segmentation methods, which are based on predicting segmentation masks, we focus at predicting the endocardial contour and the key landmark points within this contour (basal points and apex). This provides a representation that is closer to how experts perform manual annotations and hence produce results that are physiologically more plausible. Our proposed method uses a two-headed network based on the U-Net architecture. One head predicts the 7 contour points, and the other head predicts a distance map to the contour. This approach was compared to the U-Net and to a point based approach, achieving performance gains of up to 30\% in terms of landmark localisation (<4.5mm) and distance to the ground truth contour (<3.5mm).