Multi-Branch Residual Network for Ultrasonic Classification of Hepatic Steatosis with
Backscattered Signals
WANG Qian1, HUANG Yong1, WU Shuicai1, CUI Boxiang2, ZHOU Zhuhuang1
1. Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China;
2. College of Medicine, Chang Gung University, Taoyuan Taiwan 333323, China
Abstract：Objective The deep learning technology based on ultrasonic backscattered signals is an emerging trend in ultrasonic
tissue characterization, to propose a deep learning model multi-branch residual network (MBR-Net ) based on backscattered signal
convolutional neural network (CNN), and to analyze its evaluation efficiency for fatty liver. Methods MBR-Net was composed of
three branches; each branch used different convolutional blocks to enhance the ability of local feature acquisition, combined with the
residual mechanism with skip connection, which guided the network to capture features effectively. A total of 204 cases of clinical
hepatic steatosis ultrasound backscattered signals (80 cases without hepatic steatosis as S0, 70 cases with mild hepatic steatosis as S1,
36 cases with moderate hepatic steatosis as S2, and 18 cases with severe hepatic steatosis as S3) were included in the experiments.
B-mode ultrasound images were reconstructed by using the backscattered signals, the liver parenchyma area was manually selected.
For each of the radio frequency signals, a gate with a length of 768 sampling points was used to slide on it, and the step size was
20 sampling points to obtain n gating signals. Then 256 gating signals were randomly selected from n gating. Results A total of
261120 signal samples were obtained (S0: 102400; S1: 89600; S2: 46080; S3: 23040). Compared with Nguyen network and Han
network, MBR-Net had higher accuracy, sensitivity and specificity in the diagnosis of fatty liver degree ≥S1, ≥S2 and ≥S3, and
the AUC of MBR-Net was the highest. The fatty liver classification effect of MBR-Net (three-branch network ) was better than that
of two-branch network and four-branch network. Conclusion Compared with the traditional CNN method with single branch and no
residual mechanism, the MBR-Net proposed in this study has improved the classification accuracy on the whole and achieved good
performance in the classification task of evaluating the degree of hepatic steatodegeneration. MBR-Net can be used as a new method
to evaluate hepatic steatosis based on deep learning of ultrasonic backscattered signals.
王前1，黄勇1，吴水才1，崔博翔2，周著黄1. 超声背散射信号多分支残差网络评估脂肪肝[J]. 中国医疗设备, 2023, 38(6): 77-81.
WANG Qian1, HUANG Yong1, WU Shuicai1, CUI Boxiang2, ZHOU Zhuhuang1. Multi-Branch Residual Network for Ultrasonic Classification of Hepatic Steatosis with
Backscattered Signals. China Medical Devices, 2023, 38(6): 77-81.
