Boundary attention with multi-task consistency constraints for semi-supervised 2D echocardiography segmentation

Abstract

The 2D echocardiography semantic automatic segmentation technique is important in clinical applications for cardiac function assessment and diagnosis of cardiac diseases. However, automatic segmentation of 2D echocardiograms also faces the problems of loss of image boundary information, loss of image localization information, and limitations in data acquisition and annotation. To address these issues, this paper proposes a semi-supervised echocardiography segmentation method. It consists of two models: (1) a boundary attention transformer net (BATNet) and (2) a multi-task level semi-supervised model with consistency constraints on boundary features (semi-BATNet). BATNet is able to capture the location and spatial information of the input feature maps by using the self-attention mechanism. The multi-task level semi-supervised model with boundary feature consistency constraints (semi-BATNet) encourages consistent predictions of boundary features at different scales from the student and teacher networks to calculate the multi-scale consistency loss for unlabeled data. The proposed semi-BATNet was extensively evaluated on the dataset of cardiac acquisitions for multi-structure ultrasound segmentation (CAMUS) and self-collected echocardiography dataset from the First Affiliated Hospital of Chongqing Medical University. Experimental results on the CAMUS dataset showed that when only 25% of the images are labeled, the proposed method greatly improved the segmentation performance by utilizing unlabeled images, and it also outperformed five state-of-the-art semi-supervised segmentation methods. Moreover, when only 50% of the images labeled, semi-BATNet achieved the Dice coefficient values of 0.936, the Jaccard similarity of 0.881 on self-collected echocardiography dataset. Semi-BATNet can complete a more accurate segmentation of cardiac structures in 2D echocardiograms, indicating that it has the potential to accurately and efficiently assist cardiologists.

Keywords: Boundary attention module; Echocardiography semantic segmentation; Multi-task consistency constraints; Self-attention mechanism; Semi-supervised learning.