Boundary-Enhanced $U^{2}$-Net for Simultaneous Four-Chamber Segmentation in Transthoracic Echocardiography

The heart, responsible for circulating blood throughout our body, contains four chambers. Existing analysis methods primarily focus on one single ventricle. Transthoracic echocardiography provides real-time estimations of cardiac function and enables comprehensive observations of the entire heart, especially through the apical 4-chamber view. However, no current clinical indices evaluate cardiac function considering all four chambers simultaneously. Manual estimation of the four chambers is laborious, inefficient, and complicated by anatomical complexity and variable image quality, including motion artifacts and unclear borders. There is a significant need for a high-performance segmentation tool that can assess all four chambers concurrently. To address this, we collected a clinically representative dataset of 2D apical 4-chamber view echocardiograms, with annotated 4-chamber regions serving as the basis for automatic 4-chamber synergy analysis. We then proposed a boundary-enhanced network, denoted as $BeU^{2}$-Net, tailored for transthoracic echocardiography 4-chamber segmentation using our private dataset. Specifically, our network employs a two-level nested encoder-decoder architecture, utilizing a segmentation-specific residual U-block with a mixture of receptive fields at each stage to capture multi-level and multi-scale features. A dedicated boundary prediction branch, incorporating edge details, is integrated to enhance boundary segmentation performance. Experiments on both private and public datasets demonstrate that our $BeU^{2}$-Net possesses superior boundary detection capabilities and achieves high segmentation performance for echocardiographic images.