Chen Jheng-Ru, Chao Yi-Ping, Tsai Yu-Wei, Chan Hsien-Jung, Wan Yung-Liang, Tai Dar-In, Tsui Po-Hsiang
Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan.
Department of Computer Science and Information Engineering, College of Engineering, Chang Gung University, Taoyuan 333323, Taiwan.
Entropy (Basel). 2020 Sep 9;22(9):1006. doi: 10.3390/e22091006.
Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis ( < 0.05). The results indicated that physics-based information entropy for backscattering statistics analysis can be recommended for ultrasound diagnosis of hepatic steatosis, providing not only improved performance in grading but also clinical interpretations of hepatic steatosis.
熵是信号不确定性的一种定量度量，已广泛应用于超声组织表征。肝脏脂肪变性的超声评估通常涉及基于信息熵的信号后向散射统计分析。深度学习在没有任何物理假设或声学考虑的情况下提取特征进行分类。在本研究中，我们评估了信息熵和深度学习在肝脏脂肪变性分级中的临床价值。共有205名参与者接受了超声检查。图像原始数据用于香农熵成像，并由用于医学数据分析的预训练VGG-16模型进行训练和测试。将熵成像和VGG-16模型预测结果与组织学检查结果进行比较。使用受试者工作特征（ROC）曲线分析和德龙检验评估肝脏脂肪变性分级的诊断性能。使用VGG-16模型对轻度、中度和重度肝脏脂肪变性进行分级时，ROC曲线下面积分别为0.71、0.75和0.88；熵成像的ROC曲线下面积分别为0.68、0.85和0.9。随肝脏脂肪浸润而变化的超声熵在识别中度或重度肝脏脂肪变性参与者方面优于VGG-16（<0.05）。结果表明，基于物理的信息熵用于后向散射统计分析可推荐用于肝脏脂肪变性的超声诊断，不仅在分级方面性能更佳，还能对肝脏脂肪变性进行临床解读。
