MBR-Net: A multi-branch residual network based on ultrasound backscattered signals for characterizing pediatric hepatic steatosis

Qian Wang; Ming-Wei Lai; Guangyu Bin; Qiying Ding; Shuicai Wu; Zhuhuang Zhou; Po-Hsiang Tsui

doi:10.1016/j.ultras.2023.107093

MBR-Net: A multi-branch residual network based on ultrasound backscattered signals for characterizing pediatric hepatic steatosis

Ultrasonics. 2023 Dec:135:107093. doi: 10.1016/j.ultras.2023.107093. Epub 2023 Jun 27.

Authors

Qian Wang¹, Ming-Wei Lai², Guangyu Bin¹, Qiying Ding³, Shuicai Wu¹, Zhuhuang Zhou⁴, Po-Hsiang Tsui⁵

Affiliations

¹ Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China.
² Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Children's Medical Center, Chang Gung Memorial Hospital, Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Liver Research Center, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan.
³ Department of Ultrasound, BJUT Hospital, Beijing University of Technology, Beijing, China.
⁴ Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China. Electronic address: zhouzh@bjut.edu.cn.
⁵ Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan. Electronic address: tsuiph@mail.cgu.edu.tw.

PMID: 37482038
DOI: 10.1016/j.ultras.2023.107093

Abstract

The evaluation of pediatric hepatic steatosis and early detection of fatty liver in children are of critical importance. In this paper, a deep learning model based on the convolutional neural network (CNN) of ultrasound backscattered signals, multi-branch residual network (MBR-Net), was proposed for characterizing pediatric hepatic steatosis. The MBR-Net was composed of three convolutional branches. Each branch used different sizes of convolution blocks to enhance the capability of local feature acquisition, and leveraged the residual mechanism with skip connections to guide the network to effectively capture features. A total of 393 frames of ultrasound backscattered signals collected from 131 children were included in the experiments. The hepatic steatosis index was used as the reference standard for diagnosing the steatosis grade, G0-G3. The ultrasound backscattered signals within the liver region of interests (ROIs) were normalized and augmented using a sliding gate method. The gated ROI signals were randomly divided into training, validation, and test sets with the ratio of 8:1:1. The area under the operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were used as the evaluation metrics. Experimental results showed that the MBR-Net yields AUCs for diagnosing pediatric hepatic steatosis grade ≥G1, ≥G2, and ≥G3 of 0.94 (ACC: 93.65%; SEN: 89.79%; SPE: 84.48%), 0.93 (ACC: 90.48%; SEN: 87.75%; SPE: 82.65%), and 0.93 (ACC: 87.76%; SEN: 84.84%; SPE: 86.55%), respectively, which were superior to the conventional one-branch CNNs without residual mechanisms. The proposed MBR-Net can be used as a new deep learning method for ultrasound backscattered signal analysis to characterize pediatric hepatic steatosis.

Keywords: Convolutional neural network; Deep learning; Pediatric hepatic steatosis; Ultrasound backscattered signal; Ultrasound tissue characterization.

MeSH terms

Child
Fatty Liver* / diagnostic imaging
Humans
Neural Networks, Computer
Ultrasonography / methods