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Wang Q, Lai MW, Bin G, Ding Q, Wu S, Zhou Z, Tsui PH. MBR-Net: A multi-branch residual network based on ultrasound backscattered signals for characterizing pediatric hepatic steatosis. Ultrasonics 2023; 135:107093. [PMID: 37482038 DOI: 10.1016/j.ultras.2023.107093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/18/2023] [Accepted: 06/23/2023] [Indexed: 07/25/2023]
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.
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Affiliation(s)
- Qian Wang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Ming-Wei Lai
- 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
| | - Guangyu Bin
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Qiying Ding
- Department of Ultrasound, BJUT Hospital, Beijing University of Technology, Beijing, China
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Zhuhuang Zhou
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China.
| | - Po-Hsiang Tsui
- 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.
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