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Chinese Journal of Lasers, Volume. 51, Issue 18, 1801019(2024)

IVOCT Coronary Artery Calcification Plaque Segmentation Based on Convolutional Neural Network

Wei Xia1,2, Tingting Han1,2、*, Kuiyuan Tao3, Wei Wang1,2, and Jing Gao1,2
Author Affiliations
  • 1School of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China
  • 2Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin 300387, China
  • 3State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
    Equations (0)
    References (32)
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    Figures & Tables(10)
    Frame diagram of CAB-U-Net

    Fig. 1. Frame diagram of CAB-U-Net

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    Structure of CFT network

    Fig. 2. Structure of CFT network

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    Structure of ASPP

    Fig. 3. Structure of ASPP

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    Comparison of three feature pyramid structures. (a) FPN; (b) PANet; (c) BiFPN

    Fig. 4. Comparison of three feature pyramid structures. (a) FPN; (b) PANet; (c) BiFPN

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    IVOCT coordinate transformation image. (a) Polar coordinate image; (b) Cartesian coordinate image

    Fig. 5. IVOCT coordinate transformation image. (a) Polar coordinate image; (b) Cartesian coordinate image

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    Comparison of calcified plaque segmentation using different algorithms

    Fig. 6. Comparison of calcified plaque segmentation using different algorithms

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    Proposed model segmentation results of calcified plaques in OCT images under different conditions. (a) Stent implantation image; (b) thrombus image; (c) vascular branching image; (d) multiple calcified plaque image; (e) plaque missed image

    Fig. 7. Proposed model segmentation results of calcified plaques in OCT images under different conditions. (a) Stent implantation image; (b) thrombus image; (c) vascular branching image; (d) multiple calcified plaque image; (e) plaque missed image

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    CAB-U-Net automatically segments and expert annotation angles and areas of calcified plaques. (a) Angular correlation; (b) angle Bland-Altman plot; (c) area correlation; (d) area Bland-Altman plot

    Fig. 8. CAB-U-Net automatically segments and expert annotation angles and areas of calcified plaques. (a) Angular correlation; (b) angle Bland-Altman plot; (c) area correlation; (d) area Bland-Altman plot

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    • Table 1. Experimental performance of different models

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      Table 1. Experimental performance of different models

      NetworkIOUPrecisionRecallF1-score
      PSPnet［30］0.62250.74800.34910.4760
      Deeplabv3［31］0.75840.76170.62420.6861
      U-net［19］0.79920.82950.68810.7522
      SwinUnet［32］0.87480.93440.92570.9300
      TransUnet［20］0.88370.93110.94220.9366
      CAB-U-Net0.90650.93320.96620.9494

    • Table 2. Results of ablation experiment

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      Table 2. Results of ablation experiment

      NetworkIOUPrecisionRecallF1-score
      CFTASPPBiFPN
      ×××0.79920.82950.68810.7522
      √××0.87100.89000.97150.9290
      √√×0.89720.92010.97050.9446
      √×√0.90090.92540.96870.9466
      √√√0.90650.93320.96620.9494
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    Wei Xia, Tingting Han, Kuiyuan Tao, Wei Wang, Jing Gao. IVOCT Coronary Artery Calcification Plaque Segmentation Based on Convolutional Neural Network[J]. Chinese Journal of Lasers, 2024, 51(18): 1801019

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    Paper Information

    Category: laser devices and laser physics

    Received: May. 6, 2024

    Accepted: Jun. 24, 2024

    Published Online: Sep. 6, 2024

    The Author Email: Han Tingting (hanting608@163.com)

    DOI:10.3788/CJL240833

    CSTR:32183.14.CJL240833

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology