@Article{cmes.2022.018565, AUTHOR = {Jia Chen, Zhiqiang He, Dayong Zhu, Bei Hui, Rita Yi Man Li, Xiao-Guang Yue}, TITLE = {Mu-Net: Multi-Path Upsampling Convolution Network for Medical Image Segmentation}, JOURNAL = {Computer Modeling in Engineering \& Sciences}, VOLUME = {131}, YEAR = {2022}, NUMBER = {1}, PAGES = {73--95}, URL = {http://www.techscience.com/CMES/v131n1/46634}, ISSN = {1526-1506}, ABSTRACT = {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 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.}, DOI = {10.32604/cmes.2022.018565} }