EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms

TL;DR

EchoGLAD introduces a hierarchical graph neural network that leverages anatomical structure for accurate and robust detection of left ventricle landmarks in echocardiograms, outperforming prior methods especially in out-of-distribution scenarios.

Contribution

The paper proposes a novel hierarchical GNN framework with multi-level supervision for improved landmark detection in echocardiograms, addressing label sparsity and anatomical information utilization.

Findings

Achieves state-of-the-art MAE of 1.46 mm and 1.86 mm on two datasets.

Demonstrates superior out-of-distribution generalization with 4.3 mm MAE.

Outperforms prior methods in landmark detection accuracy.

Abstract

The functional assessment of the left ventricle chamber of the heart requires detecting four landmark locations and measuring the internal dimension of the left ventricle and the approximate mass of the surrounding muscle. The key challenge of automating this task with machine learning is the sparsity of clinical labels, i.e., only a few landmark pixels in a high-dimensional image are annotated, leading many prior works to heavily rely on isotropic label smoothing. However, such a label smoothing strategy ignores the anatomical information of the image and induces some bias. To address this challenge, we introduce an echocardiogram-based, hierarchical graph neural network (GNN) for left ventricle landmark detection (EchoGLAD). Our main contributions are: 1) a hierarchical graph representation learning framework for multi-resolution landmark detection via GNNs; 2) induced hierarchical supervision at different levels of granularity using a multi-level loss. We evaluate our model on a public and a private dataset under the in-distribution (ID) and out-of-distribution (OOD) settings. For the ID setting, we achieve the state-of-the-art mean absolute errors (MAEs) of 1.46 mm and 1.86 mm on the two datasets. Our model also shows better OOD generalization than prior works with a testing MAE of 4.3 mm.