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Brain tumor image segmentation based on improved Polyp-PVT

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Abstract

Deep learning models have shown powerful feature extraction capabilities in brain tumor segmentation tasks to use medical image information and help doctors make disease diagnoses. However, due to the diverse shapes of brain tumors and the variability of brain imaging, the segmentation effect of the model on brain tumors has specific room for improvement. The Polyp-PVT network is adopted as the basic model based on its advantages of fast running speed and high segmentation accuracy. The attentional selective fusion (ASF) module is introduced to replace the CFM module. The channel shuffle module is introduced before the ASF module—PVT-ASF-CS network is proposed. This is helpful to improve the ability of feature fusion and segmentation effect of the network. At the same time, information mixing between channels can be completed without increasing the computation and parameter quantity. Based on the Brats2019 dataset, the ablation experiment of the PVT-ASF-CS network was conducted and compared with other models. The Dice coefficient and HD were used as evaluation indexes to evaluate the segmentation effect of the model. The experimental results show that the proposed PVT-ASF-CS model can extract more adequate and detailed features, and has a better segmentation effect on brain tumors.

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Data availability

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

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Funding

This research was received by the natural science foundation of Heilongjiang Province (No. LH2020F033), the national natural science youth foundation of China (No.11804068) and research project of the Heilongjiang Province Health Commission (No. 20221111001069).

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Author notes

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

Authors and Affiliations

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

    Chaofeng Lan, Xinyu Yu & Xiuhuan Mao

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

    Lei Zhang

  3. Scientific Research Center, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150080, China

    Yan Xia

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

    Meng Zhang

Authors
  1. Chaofeng Lan
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  2. Xinyu Yu
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  3. Lei Zhang
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  4. Yan Xia
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  5. Xiuhuan Mao
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  6. Meng Zhang
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Contributions

CL contributed to the conception of the study and contributed significantly to analysis and manuscript preparation; XY and MZ made important contributions in making adjustments to the structure of the paper, revising the paper, editing the manuscript and English polished; LZ polished, edited and checked the article during the submission process; YX carried on the data analysis of the paper; XM performed the experiment, the data analyses and wrote the manuscript. All authors reviewed the manuscript.

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Correspondence to Lei Zhang, Yan Xia or Meng Zhang.

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Lan, C., Yu, X., Zhang, L. et al. Brain tumor image segmentation based on improved Polyp-PVT. SIViP 17, 4019–4027 (2023). https://doi.org/10.1007/s11760-023-02632-w

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  • DOI: https://doi.org/10.1007/s11760-023-02632-w

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