Abstract

Info

Title:: Liver capsule segmentation in ultrasound image using edge supervision based network

Author(s):: PU Xiuli1; LIU Xiang1; TANG Xian1; SONG Jialin2; 1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Department of Ultrasound Diagnosis and Treatment, Shanghai Changzheng Hospital, the Second Military Medical University, Shanghai 200003, China

Keywords:: Keywords: UNet liver capsule edge supervision atrous convolution image segmentation

Abstract:: Abstract: The early detection of liver fibrosis and liver cirrhosis is of great significance for clinical treatment and prognosis evaluation, and the morphological and texture characteristics of liver capsule are important for the computer-assisted diagnosis of liver cirrhosis. An edge supervision based network (ES-UNet) is proposed for liver capsule segmentation in ultrasound images. Based on the commonly used segmentation model (UNet), ES-UNet uses atrous convolution to expand the receptive field, and edge supervision module to focus the feature learning on the region with large image gradient. In addition, a mixed weighted loss function is used to reduce the extreme imbalance between the liver capsule and other regions. The experimental results show that compared with those of original UNet model, the average Dice coefficient of ES-UNet is increased by 0.171 5, and the mean intersection over union is higher by 0.021 5, and the other indicators are also elevated significantly, indicating that each component of the proposed algorithm has a certain contribution to the optimization of model segmentation performance. The improved model can achieve accurate liver capsule segmentation.