Vol.131, No.1, 2022, pp.73-95, doi:10.32604/cmes.2022.018565
OPEN ACCESS
ARTICLE
Mu-Net: Multi-Path Upsampling Convolution Network for Medical Image Segmentation
  • Jia Chen1, Zhiqiang He1, Dayong Zhu1, Bei Hui1,*, Rita Yi Man Li2, Xiao-Guang Yue3,4,5
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, Hong Kong, 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
* Corresponding Author: Bei Hui. Email:
Received 02 August 2021; Accepted 21 October 2021; Issue published 24 January 2022
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.
Keywords
Medical image segmentation; MU-Net (multi-path upsampling convolution network); U-Net; clinical diagnosis; encoder-decoder networks
Cite This Article
Chen, J., He, Z., Zhu, D., Hui, B., Yi, R. et al. (2022). Mu-Net: Multi-Path Upsampling Convolution Network for Medical Image Segmentation. CMES-Computer Modeling in Engineering & Sciences, 131(1), 73–95.
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