Multi-scale Wavelet Network Algorithm for Pediatric Echocardiographic Segmentation via Feature Fusion

The analysis and segmentation echocardiography is the critical steps for early diagnosis and timely treatment of congenital heart disease. However, the existing segmentation algorithms have the issues of information loss and low utilization of detail information, which reduces automatic segmentation accuracy. To solve this, we propose a multi-scale wavelet network (MS-Net) for pediatric echocardiographic segmentation. The MS-Net includes two branches: wavelet Unet (W-Unet) and bidirectional feature fusion (BFF-Net). In MS-Net, the discrete wavelet transform (DWT) is used to replace the sampling operation to solve information loss. In the first branch, BFF-Net achieves the fusion of context and detail information in low-resolution images by setting two top-down paths and a bottom-up path. In the second branch, W-Unet focuses on the extraction of high-resolution image details by reducing the network depth and propagation method. The information processing of the two branches is realized by feature fusion to solve the low utilization of detail information. The subjective and objective analysis on our self-collected pediatric echocardiographic dataset verify that the proposed algorithm achieves better segmentation accuracy than other commonly used algorithms.