Objective: To establish automatic liver fibrosis classification models by using traditional machine learning and deep learning methods and preliminaryly evaluate the efficiency. Methods: Gray scale ultrasound images and corresponding elastic images of 354 patients, 247 males and 107 females, mean age (54±12) years undergoing partial hepatectomy in Zhongshan Hospital of Fudan University from November 2014 to January 2016 were enrolled in this study. By using traditional machine learning and deep learning methods, an automatic classification model of liver fibrosis stages (S0 to S4) were established through feature extraction and classification of ultrasound image data sets and the accuracy in different classification categories of each model were calculated, by using liver biopsy as the reference standard. Results: Pathological examination showed 73 cases in pathological stage S0, 40 cases in S1, 49 cases in S2, 41 cases in S3, and 151 cases in S4. The traditional machine classification model based on support vector machine (SVM) classifier and sparse representation classifier and the deep learning classification model based on LeNet-5 neural network, their accuracy rates in the two categories (S0/S1/S2 and S3/S4) were 89.8%, 91.8% and 90.7% respectively; the accuracy rates in the three categories (S0/S1 and S2/S3 and S4) were 75.3%, 79.4% and 82.8% respectively; the accuracy in the three categories (S0 and S1/S2/S3 and S4) were 79.3%, 82.7% and 87.2% respectively. Conclusions: Computer-aided assessment of liver fibrosis progression in patients with chronic hepatitis B has a high accuracy, and can achieve a more detailed classification. This method is expected to be applied in the non-invasive evaluation of liver fibrosis in patients with hepatitis B in clinical work in the future.
目的： 应用传统机器学习和深度学习方法建立计算机辅助诊断肝纤维化自动分类模型，并初步评估其效果。 方法： 选取2014年11月至2016年1月于复旦大学附属中山医院接受肝部分切除术和术前接受肝脏剪切波弹性成像检查的354例患者的灰阶超声图像和相应弹性图像，男247例、女107例，平均年龄（54±12）岁，以病理学诊断肝纤维化分级（S0~S4）为"金标准"，利用传统机器学习和深度学习的方法，对超声图像数据集进行特征提取和分类，建立肝纤维化自动分类模型，统计每种模型不同分类情景的准确率。 结果： 病理学检查显示肿块周边肝实质病理分期S0者73例，S1者40例，S2者49例，S3者41例，S4者151例。基于支持向量机分类器和稀疏表示分类器的传统机器分类模型和基于LeNet-5神经网络的深度学习分类模型，在二分类（S0/S1/S2与S3/S4）的准确率分别为89.8%、91.8%和90.7%；在三分类（S0/S1、S2/S3与S4）的准确率分别为75.3%、79.4%和82.8%；在三分类（S0、S1/S2/S3与S4）的准确率分别为79.3%、82.7%和87.2%。 结论： 计算机辅助诊断慢性乙肝患者肝纤维化进程准确性较高，且可以做到更细化的肝纤维化进程分类。未来有望应用于无创评估乙肝患者肝纤维化进程的临床工作中。.
Keywords: Artificial intelligence; Elasticity imaging techniques; Liver cirrhosis; Ultrasonography.