Abstract

Info

Author(s):: MO Chunmei1; 2; ZHOU Jinzhi1; 2; LI Xue1; 2; YU Xi1; 2; 1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China 2. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621000, China

Keywords:: Keywords: liver segmentation U-Net residual block skip connection mixed loss function

Abstract:: Abstract: In order to solve the problem of low precision in existing methods for liver segmentation, a liver segmentation method based on improved U-Net is proposed. U-Net structure is improved by introducing improved residual block and redesigning skip connection, and then mixed loss function is adopted to enhance the utilization of feature information and reduce the semantic differences between encoder and decoder, thereby alleviating class imbalance problem and speeding up network convergence. The experimental results on Liver Tumor Segmentation (LITS), a common data set provided by CodaLab, showed that the Dice similarity coefficient, sensitivity and intersection over union achieved by the proposed method were 93.69%, 94.87% and 87.49%, respectively. Compared with other segmentation methods, such as U-Net and Attention U-Net, the proposed method can obtain a more accurate result in liver segmentation and has better segmentation performance.