JANet: A joint attention network for balancing accuracy and speed in left ventricular ultrasound video segmentation

Abstract

Multiple cardiac diseases are closely associated with functional parameters of the left ventricle, but functional parameter quantification still requires manual involvement, a time-consuming and less reproducible task. We develop a joint attention network (JANet) and expand it into two versions (V1 and V2) that can be used to segment the left ventricular region in echocardiograms to assist physicians in diagnosis. V1 is a smaller model with a size of 56.3 MB, and V2 has a higher accuracy. The proposed JANet V1 and V2 achieve a mean dice score (DSC) of 93.59/93.69(V1/V2), respectively, outperforming the state-of-the-art models. We grade 1264 patients with 87.24/87.50 (V1/V2) accuracy when using the 2-level classification criteria and 83.62/84.18 (V1/V2) when using the 5-level classification criteria. The results of the consistency analysis show that the proposed method is comparable to that of clinicians.