Chinese Journal of Lasers, Volume. 51, Issue 21, 2107109(2024)

Large‑Deformation 3D Medical Image Registration Based on Multi‑Scale Constraints

Yu Shen1... Ziyi Wei1,*, Yuan Yan2, Shan Bai1, Yangyang Li1, Bohao Li1, Baoqu Gao1, Zhenkai Qiang1 and Jiarong Yan1 |Show fewer author(s)
Author Affiliations
  • 1School of Electronic and Information Engineering, Lanzhou 730070, Gansu , China
  • 2Northwest Research Institute Co., Ltd. of C.R.E .C, Lanzhou 730099, Gansu , China
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    Figures & Tables(14)
    Overall framework diagram
    MC-Net registration model diagram
    Schematic diagram of multi-scale constraint loss function
    Preprocessing diagrams
    Registration results of OASIS and LPBA40 datasets. (a)(e) Moving images to be registered; (b)(f) fixed reference images;
    Registration results of Abdomen CT-CT dataset. (a) Moving images to be registered; (b) fixed reference images; (c) registered images; (d) label maps of the moving images to be registered; (e) label maps of the fixed reference images; (f) label maps of the registered images
    Multi scale visualization of deformation field. (a) Moving images to be registered; (b) deformation fields (voxel: 160×192×160); (c) deformation fields (voxel: 80×96×80); (d) deformation fields (voxel: 40×48×40); (e) fixed reference images
    Comparison of parameter quantity before and after multi-core fusion
    Registration results of different methods on LPBA40 dataset. (a) Moving images to be registered; (b) fixed reference images; (c) registration results of ANTs; (d) registration results of VoxelMorph; (e) registration results of CycleMorph; (f) registration results of TransMorph; (g) registration results of MC-Net
    Registration results of different methods on the Abdomen CT-CT dataset. (a) Moving images to be registered; (b) fixed reference images; (c) registration results of ANTs; (d) registration results of Elastix; (e) registration results of VoxelMorph; (f) registration results of TransMorph; (g) registration results of MC-Net
    Ablation experimental results on the LPBA40 dataset. (a) Moving images to be registered; (b) registration results of U-Net; (c) registration results of U-Net+MK; (d) registration results of U-Net+MK+CBAM; (e) fixed reference images
    • Table 1. Evaluation results of OASIS and LPBA40 datasets

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      Table 1. Evaluation results of OASIS and LPBA40 datasets

      DatasetTime /sDice coefficientHD95
      OASIS0.520.8422.415
      LPBA400.360.8226.126
    • Table 2. Registration results comparison of different methods on LPBA40 dataset

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      Table 2. Registration results comparison of different methods on LPBA40 dataset

      MethodTime /sAmount of calculationDice coefficientHD95
      ANTs93.10.7966.295
      VoxelMorph0.3093964570.7836.355
      CycleMorph0.4563612990.7406.479
      TransMorph0.4161079499230.8036.200
      MC-Net0.36342667070.8226.126
    • Table 3. Results of ablation experiments on LPBA40 dataset

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      Table 3. Results of ablation experiments on LPBA40 dataset

      U-NetMKCBAMDice coefficient
      ××0.583
      ×0.764
      0.822
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    Yu Shen, Ziyi Wei, Yuan Yan, Shan Bai, Yangyang Li, Bohao Li, Baoqu Gao, Zhenkai Qiang, Jiarong Yan. Large‑Deformation 3D Medical Image Registration Based on Multi‑Scale Constraints[J]. Chinese Journal of Lasers, 2024, 51(21): 2107109

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    Paper Information

    Category: Biomedical Optical Imaging

    Received: Sep. 2, 2024

    Accepted: Oct. 6, 2024

    Published Online: Oct. 31, 2024

    The Author Email: Wei Ziyi (17339821643@163.com)

    DOI:10.3788/CJL241180

    CSTR:32183.14.CJL241180

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