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Segmentation of brain tumor MRI image based on improved attention module Unet network

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Abstract

In recent years, the fully convolutional network represented by Unet has been widely used in the field of medical image segmentation. However, due to the diversity of the shapes of lesions and the differences in the structures of different organs, the segmentation of lesions using only Unet structure cannot meet the requirements of accuracy and speed. Therefore, an improved Unet network for brain tumor segmentation is proposed. To reduce the number of parameters while extracting richer features and improving the accuracy of segmentation, this article introduces the inverted residuals block to replace the convolution module in the encoding and decoding stages of Unet to improve the calculation speed and accuracy; to better combine high-order semantic information with low-order semantic information, improve for the quality of detailed features in the training process, an improved Residuals Convolutional Block Attention Module is added between the encoder and the decoder. Combining the above two points of improvement, this article proposes an improved model based on Unet. Based on the Brats2019 dataset, an ablation experiment was performed on the proposed improved Unet model and compared with the TrUE-Net, ConResNet and OM-Net methods, and the Dice coefficient and Hausdorff distance were used as evaluation indicators to analyze the segmentation effect of the model. The experimental results show that the Dice coefficient of the improved Unet network model proposed in this article is 0.020–0.027 higher than other comparative models on average, and the Haushofer distance is reduced by 2.67–10.06.

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All data included in this study are available upon request by contact with the corresponding author.

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Funding

This research received by the natural science foundation of Heilongjiang Province (No. LH2020F033) and the national natural science youth foundation of china (No.11804068).

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Author notes

  1. Lei Zhang, Chaofeng Lan and Xiuhuan Mao contributed equally to this work.

Authors and Affiliations

  1. Beidahuang Industry Group General Hospital, Harbin, 150088, China

    Lei Zhang

  2. School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin, 150080, China

    Chaofeng Lan & Xiuhuan Mao

  3. Mechanical and Electrical Engineering College, Hainan University, Haikou, 570228, China

    Lirong Fu

  4. School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, 510006, China

    Meng Zhang

Authors
  1. Lei Zhang
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  2. Chaofeng Lan
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  3. Lirong Fu
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  4. Xiuhuan Mao
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  5. Meng Zhang
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Contributions

Lei Zhang and Chaofeng Lan contributed to the conception of the study and contributed significantly to analysis and manuscript preparation; Lirong Fu and Meng Zhang made important contributions in made adjustments to the structure of the paper, revised the paper, edited the manuscript and english polished; Xiuhuan Mao performeds the experiment、the data analyses and wrote the manuscript. All authors reviewed the manuscript.

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Correspondence to Chaofeng Lan or Meng Zhang.

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Zhang, L., Lan, C., Fu, L. et al. Segmentation of brain tumor MRI image based on improved attention module Unet network. SIViP 17, 2277–2285 (2023). https://doi.org/10.1007/s11760-022-02443-5

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  • DOI: https://doi.org/10.1007/s11760-022-02443-5

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