Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approach

TL;DR

This paper introduces a CNN-based method using a modified U-Net architecture to automate and improve the accuracy of 2D echocardiography measurements of the left ventricle, addressing variability issues in clinical practice.

Contribution

It presents a novel CNN approach with anatomically meaningful heatmaps and a multi-component loss function for precise LV measurement in ultrasound images.

Findings

Achieves low mean percent error in LV measurements.

Outperforms other neural networks in accuracy.

Approaches intra-analyzer expert error levels.

Abstract

Two-dimensional echocardiography (2DE) measurements of left ventricle (LV) dimensions are highly significant markers of several cardiovascular diseases. These measurements are often used in clinical care despite suffering from large variability between observers. This variability is due to the challenging nature of accurately finding the correct temporal and spatial location of measurement endpoints in ultrasound images. These images often contain fuzzy boundaries and varying reflection patterns between frames. In this work, we present a convolutional neural network (CNN) based approach to automate 2DE LV measurements. Treating the problem as a landmark detection problem, we propose a modified U-Net CNN architecture to generate heatmaps of likely coordinate locations. To improve the network performance we use anatomically meaningful heatmaps as labels and train with a multi-component loss function. Our network achieves 13.4%, 6%, and 10.8% mean percent error on intraventricular septum (IVS), LV internal dimension (LVID), and LV posterior wall (LVPW) measurements respectively. The design outperforms other networks and matches or approaches intra-analyser expert error.