Jia Chen1, Zhiqiang He1, Dayong Zhu1, Bei Hui1,*, Rita Yi Man Li2, Xiao-Guang Yue3,4,5
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 73-95, 2022, DOI:10.32604/cmes.2022.018565
- 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 More >
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