Convolutional neural network classification of ultrasound parametric images based on echo-envelope statistics for the quantitative diagnosis of liver steatosis.

Abstract

Early detection and quantitative evaluation of liver steatosis are crucial. Therefore, this study investigated a method for classifying ultrasound images to fatty liver grades based on echo-envelope statistics (ES) and convolutional neural network (CNN) analyses. Three fatty liver grades, i.e., normal, mild, and moderate-to-severe, were defined using the thresholds of the magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF). There were 10 cases of each grade, totaling 30 cases. To visualize the texture information affected by the deposition of fat droplets within the liver, the maps of first- and fourth-order moments and the heat maps formed from both moments were employed as parametric images derived from the ES. Several dozen to hundreds of regions of interest (ROIs) were extracted from the liver region in each parametric image. A total of 7680 ROIs were utilized for the transfer learning of a pretrained VGG-16 and classified using the transfer-learned VGG-16. The classification accuracies of the ROIs in all types of the parametric images were approximately 46%. The fatty liver grade for each case was determined by hard voting on the classified ROIs within the case. In the case of the fourth-order moment maps, the classification accuracy of the cases through hard voting mostly increased to approximately 63%. The formation of parametric images derived from the ES and the CNN classification of the parametric images were proposed for the quantitative diagnosis of liver steatosis. In more than 60% of the cases, the fatty liver grade could be estimated solely using ultrasound images.