A Comparison of Deep Learning Models for Automatic Left-Ventricular Segmentation in 3D Echocardiography

Echocardiography is a non-invasive, non-ionizing, and cost-effective medical imaging modality that uses ultrasound waves to evaluate cardiac function. Left ventricle (LV) analysis is crucial for diagnosing cardiac diseases. Segmentation of the LV from echocardiography images is a challenging, time-consuming process requiring manual contouring from experts and is prone to inter-observer variability. Five different deep-learning models are evaluated for automatic LV segmentation from 3D echocardiography. The models were compared using overlap and distance metrics: Dice score, Jaccard index, and Hausdorff distance. Volumetric analysis was used to examine the accuracy of the predictions from the deep learning models against the expert-annotated ground truth volumes. The comparison between these models provides a foundation for further development of accurate and efficient automated LV segmentation methods, particularly approaches that can leverage the temporal consistency of echocardiography scans.