Chen, JiaJiaChenHe, ZhiqiangZhiqiangHeZhu, DayongDayongZhuHui, BeiBeiHuiProf. LI Yi Man, RitaRitaProf. LI Yi ManYue, Xiao-GuangXiao-GuangYue2022-02-142022-02-142022CMES - Computer Modeling in Engineering and Sciences, 2022, vol. 130(3), pp. 73-95.1526-14921526-1506http://hdl.handle.net/20.500.11861/6906Open accessMedical image segmentation plays an important role in clinical diagnosis, quantitative analysis, and treatment process. Since 2015, U-Net-based approaches have been widely used for medical 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 segmentation more 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.enPeer Reviewed Journal Article10.32604/cmes.2022.018565