Multi-directional Attention Network for Segmentation of Pediatric Echocardiographic.

Accurate segmentation of key anatomical structures in pediatric echocardiography is essential for the diagnosis and treatment of congenital heart disease. However, most of the existing segmentation methods for echocardiography have the problem of loss of detailed information, which has a certain impact on the accuracy of segmentation. Based on this, we propose a multi-directional attention (MDA) network for echocardiographic segmentation. This method uses U-Net as the backbone network to extract the initial features of different layers, and then sends the initial features to our proposed MDA module for feature enhancement. Among them, MDA includes two parts: First, considering the different contribution rates of spatial information in different directions to features, we construct a multi-directional spatial attention (MDSA) module to extract spatial information in different directions. Then to avoid the loss of channel information, we construct a channel weight constraint module (CWC) to constrain the weight of the spatial features extracted by MDSA. Finally, the group fusion feature output by MDA is used as the input of the decoder, and the final segmentation prediction result is obtained by setting the layered feature fusion (LFF) module. We conduct an extensive evaluation of 4,485 two-dimensional (2D) pediatric echocardiograms from 127 echocardiographic videos. Experiments show that the proposed algorithm can achieve the results of pediatric echocardiographic anatomical structures (left ventricle (LV), left atrium (LA)) with the average dice, precision, and recall were 0.9346, 0.9370, and 0.9406.