Abstract

Info

Title:: Liver segmentation algorithm based on conditional parametric attention network

Author(s):: ZHAO Haohui1; GAO Yongbin1; YANG Shuqun1; HU Xiaojun2; FAN Yingfang3; 1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Department of Hepatobiliary Surgery, the Fifth Affiliated Hospital of Southern Medical University, Guangzhou 510000, China 3. Hepatobiliary Department 1, Zhujiang Hospital of Southern Medical University, Guangzhou 510000, China

Keywords:: Keywords: liver segmentation convolutional neural network conditional parametric convolution CPat-Net

Abstract:: Abstract: In view of the low contrast, fuzzy boundary and poor segmentation results in the existing liver CT image segmentation algorithms, a conditional parametric attention network (CPat-Net) is presented. The method uses conditional parametric convolution to replace the conventional convolution in the residual network, and integrates the fused conditional residual convolution module into the encoder for improving model capacity and maintaining efficient computation. Then the spatial and channel attention mechanisms in the CPat module are used to obtain the semantic and detail information of the feature map, so as to better combine the local features with their global dependencies, and finally depth supervision is adopted to fuse multi-scale semantic information for improving the segmentation performance and robustness. The experiment reveals that the method has a Dice similarity cofficient, intersection over union and Jaccrad coefficient of 94.1%, 90.3% and 92.4% on the liver CT image data set. Compared with the advanced methods such as UNet, CENet and CSNet, the proposed method has a higher accuracy in liver segmentation.