Detection for Liver Fibrosis Based on Computer Simulation by Deep Learning

Liver fiber is an intermediate and reversible link in the process of cirrhosis, early detection and intervention of liver fibrosis is of great significance for the development and prognosis. Ultrasound as a one of the common diagnostic methods for liver fibers has many advantages like convenience, great accuracy and robustness. However, it is difficult to get subjective and uniform diagnoses because of the ultrasound images can be inevitably affected by the device characteristics, the interactions between ultrasound and body tissues, operation approaches and other uncontrollable factors. So we proposed a new liver fibrosis detecting algorithm based on the ultrasound echo amplitude analysis and a deep learning to classify normal and fibrosis tissue in computer simulation data. In order to study the relationship between scatterer density and hepatic fibrosis, we simulated various scatterer density liver fibrosis ultrasound image by creating random scatterer field and convolving with point spread function. Compared with the detection of traditional statistical analysis and parameter imaging, we use the data of echo amplitude distribution and image gray histogram distribution to classify the category of the window by CNN. The result shows that CNN can provide a better performance in classification and prediction than parameter imaging.