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.
Highlights
The purpose of medical image segmentation is to aid radiologists and clinicians in making the diagnostic and treatment process more efficient
To overcome the shortcomings in the U-Net series network for medical image segmentation, we propose the MU-Net, a new segmentation architecture based on multi-path upsampling and skip connections
The experimental results show that the proposed architecture is effective and greatly reduces the number of parameters in the model it can improve the performance of U-Net series networks
Summary
The purpose of medical image segmentation is to aid radiologists and clinicians in making the diagnostic and treatment process more efficient. Based on fully convolutional networks (FCN) [8] and U-Net [9], CNNs have made significant improvements in biomedical image segmentation. High-level, deep, coarsegrained feature maps from the expansive path (decoder) can be combined with low-level, shallow, fine-grained feature maps from the contracting path (encoder) through skip connections. To overcome the shortcomings in the U-Net series network for medical image segmentation, we propose the MU-Net, a new segmentation architecture based on multi-path upsampling and skip connections. The experimental results show that the proposed architecture is effective and greatly reduces the number of parameters in the model it can improve the performance of U-Net series networks. The contributions of our work can be summarized as follows: 1) Based on the U-Net series, we propose a novel convolutional network MU-Net for medical image segmentation, using multi-expansive paths to implement upsampling.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
