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Deep learning-based 3D brain multimodal medical image registration

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Abstract

Medical image registration is a critical preprocessing step in medical image analysis. While traditional medical image registration techniques have matured, their registration speed and accuracy still fall short of clinical requirements. In this paper, we propose an improved VoxelMorph network incorporating ResNet modules and CBAM (RCV-Net), for 3D multimodal unsupervised registration. Unlike popular convolution-based U-shaped registration networks like VoxelMorph, RCV-Net incorporates the convolutional block attention module (CBAM) during the convolution process. This inclusion enhances the feature map information extraction capabilities during training and effectively prevents information loss. Additionally, we introduce a lightweight and residual network module at the network’s base, which enhances learning ability without significantly increasing training parameters. To evaluate the superiority of our registration model, we utilize evaluation metrics such as structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE). Experimental results demonstrate that our proposed network structure outperforms current state-of-the-art methods, yielding better performance in multimodal registration tasks. Furthermore, generalization testing on databases outside of the training set has confirmed the optimal registration effectiveness of our model.

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Data availability

The Brats 2019 dataset used during the study is a publicly available dataset from the Multimodal Brain Tumour Segmentation Challenge 2019, [https://grand-challenge.org/challenges/].

The Brats 2021 dataset used during the study is a publicly available dataset from the Multimodal Brain Tumour Segmentation Challenge 2021, [https://grand-challenge.org/challenges/].

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Funding

The work was supported by the National Science Foundation for Young Scientists of China (Grant No.61806060), 2019–2022, and the Natural Science Foundation of Heilongjiang Province (LH2019F024), China, 2019–2022; and Basic and Applied Basic Research Foundation of Guangdong Province (2021A1515220140) and the Youth Innovation Project of Sun Yat-sen University Cancer Center (QNYCPY32).

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Authors and Affiliations

  1. Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080, Heilongjiang, China

    Liwei Deng, Qi Lan & Qiang Zhi

  2. School of Computer Science and Technology, Harbin University of Science and Technology, HarbinHeilongjiang, 150080, China

    Liwei Deng

  3. Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China

    Sijuan Huang & Xin Yang

  4. Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, Guangdong, China

    Jing Wang

Authors
  1. Liwei Deng
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  2. Qi Lan
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  3. Qiang Zhi
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  4. Sijuan Huang
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  5. Jing Wang
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  6. Xin Yang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by LD, QL, QZ, SH, XY, and JW The first draft of the manuscript was written by LD, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jing Wang or Xin Yang.

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Informed consent was obtained from all individual participants included in the study.

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The authors declare no competing interests.

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Deng, L., Lan, Q., Zhi, Q. et al. Deep learning-based 3D brain multimodal medical image registration. Med Biol Eng Comput 62, 505–519 (2024). https://doi.org/10.1007/s11517-023-02941-9

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