TY - EJOU AU - Chen, Jia AU - He, Zhiqiang AU - Zhu, Dayong AU - Hui, Bei AU - Li, Rita Yi Man AU - Yue, Xiao-Guang TI - Mu-Net: Multi-Path Upsampling Convolution Network for Medical Image Segmentation T2 - Computer Modeling in Engineering \& Sciences PY - 2022 VL - 131 IS - 1 SN - 1526-1506 AB - 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. KW - Medical image segmentation; MU-Net (multi-path upsampling convolution network); U-Net; clinical diagnosis; encoder-decoder networks DO - 10.32604/cmes.2022.018565