Mu-Net:Multi-Path Upsampling Convolution Network for Medical Image Segmentation  

作　　者：Jia Chen Zhiqiang He Dayong Zhu Bei Hui Rita Yi Man Li Xiao-Guang Yue 

机构地区：[1]School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu,610054,China [2]Department of Economics and Finance/Sustainable Real Estate Research Center,Hong Kong Shue Yan University,Hongkong,China [3]Rattanakosin International College of Creative Entrepreneurship,Rajamangala University of Technology Rattanakosin,Nakhon Pathom,73170,Thailand [4]Department of Computer Science and Engineering,School of Sciences,European University Cyprus,Nicosia,1516,Cyprus [5]CIICESI,ESTG,Polit′ecnico do Porto,4610-156,Felgueiras,Portugal

出　　处：《Computer Modeling in Engineering & Sciences》2022年第4期73-95,共23页工程与科学中的计算机建模（英文）

基　　金：The authors received Sichuan Science and Technology Program(No.18YYJC1917)funding for this study.

摘　　要：Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment process.Since 2015,U-Net-based approaches have been widely used formedical image segmentation.The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps.However,the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information.More high-level information can make the segmentationmore accurate.In this paper,we propose MU-Net,a novel,multi-path upsampling convolution network to retain more high-level information.The MU-Net mainly consists of three parts:contracting path,skip connection,and multi-expansive paths.The proposed MU-Net architecture is evaluated based on three different medical imaging datasets.Our experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different datasets.At the same time,the computational efficiency is significantly improved by reducing the number of parameters by more than half.

关 键 词：Medical image segmentation MU-Net(multi-path upsampling convolution network) U-Net clinical diagnosis encoder-decoder networks 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]