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Residual UNet with spatial and channel attention for automatic magnetic resonance image segmentation of rectal cancer

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Abstract

The precise segmentation of rectal tumors is a key step in the diagnosis and treatment of rectal cancer. This paper aims to study the automatic segmentation task of rectal tumors based on deep learning methods, and proposes a residual UNet network model that combines spatial attention and channel attention. The model uses residual convolution for feature extraction, and uses squeeze-and-excitation module and attention gating module to focus on more useful features. In this study, we established a rectal tumor dataset for model evaluation, and used a combination of two-class cross-entropy and DICE loss function in the training process. Comparative experiments show that the DICE similarity coefficient is 0.8476, the Hausdorff distance reaches 9.5622, the prediction accuracy of the model is 0.9938, and the evaluation indicators are better than the segmentation results of UNet and AttUNet, which can effectively segment the rectal tumor area, and the combined loss function can also improve the segmentation accuracy by about 15% to a certain extent.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No.61971253); Shandong Provincial Natural Science Foundation, China (Grant No.ZR2014FL026).

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Authors and Affiliations

  1. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China

    Mingjia Wang, YuCui Chen & Baozhu Qi

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  1. Mingjia Wang
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  2. YuCui Chen
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Correspondence to Mingjia Wang.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled is ‘Residual UNet with Spatial and Channel Attention for Automatic Magnetic Resonance Image Segmentation of Rectal Cancer’.

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Wang, M., Chen, Y. & Qi, B. Residual UNet with spatial and channel attention for automatic magnetic resonance image segmentation of rectal cancer. Multimed Tools Appl 81, 43821–43835 (2022). https://doi.org/10.1007/s11042-022-13256-6

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  • DOI: https://doi.org/10.1007/s11042-022-13256-6

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