• Laser & Optoelectronics Progress
  • Vol. 61, Issue 18, 1837005 (2024)
Wenyue Hao1, Huaiyu Cai1,*, Tingtao Zuo2, Zhongwei Jia3..., Yi Wang1 and Xiaodong Chen1|Show fewer author(s)
Author Affiliations
  • 1Key Laboratory of Optoelectronics Information Technology, Ministry of Education, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
  • 2Lepu Medical Technology (Beijing) Co., Ltd., Beijing 102200, China
  • 3Southwestern Lu Hospital, Liaocheng 252325, Shandong, China
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    DOI: 10.3788/LOP232774 Cite this Article Set citation alerts
    Wenyue Hao, Huaiyu Cai, Tingtao Zuo, Zhongwei Jia, Yi Wang, Xiaodong Chen. Self-Supervised Pre-Training for Intravascular Ultrasound Image Segmentation Method Based on Diffusion Model[J]. Laser & Optoelectronics Progress, 2024, 61(18): 1837005 Copy Citation Text show less
    Schematic diagram of example of encoder-decoder structure[26]
    Fig. 1. Schematic diagram of example of encoder-decoder structure[26]
    Denoising self-supervised pre-training paradigm based on diffusion
    Fig. 2. Denoising self-supervised pre-training paradigm based on diffusion
    Comparison of different initialization methods of 20% data set
    Fig. 3. Comparison of different initialization methods of 20% data set
    Comparison of visualization results of segmentation results (yellow line on the outside represents the segment of the media and green line on the inside represents the segment of the lumen)
    Fig. 4. Comparison of visualization results of segmentation results (yellow line on the outside represents the segment of the media and green line on the inside represents the segment of the lumen)

    算法1基于扩散的去噪自监督代理任务伪代码

    输入:

       xt:输入图像

    输出:

       ϵθ(xt):输出图像

    算法流程:

    1:repeat

    2: x0~q(x0)

    3: t~Uniform({1,,T})

    4: ϵ~N(0,1)

    5: take gradient desent step onθ||ϵθ(αt¯x0+1-αt¯ϵ)-ϵ||2

    6:until converged

    Table 0. [in Chinese]
    MethodDDice(↑)JJM(↑)HHD(↓)PPAD(↓)
    LumenMediaLumenMediaLumenMediaLumenMedia
    Scratch(20%)0.9020.8750.8310.7920.3850.4500.1950.301
    Pred_x(20%)0.9210.9330.8570.8780.1820.2520.1070.104
    Pred_noise(20%)0.9320.9420.8750.8930.1690.3430.0880.082
    Scratch(100%)0.9320.9490.8770.9060.2260.2680.0920.078
    Table 1. Comparison of results of different initialization methods
    ModelDDice(↑)JJM(↑)HHD(↓)PPAD(↓)
    LumenMediaLumenMediaLumenMediaLumenMedia
    Unet(100%)0.9290.9440.8710.8960.2500.3910.0880.090
    Deeplabv3+(100%)0.9290.9490.8710.9050.1990.1860.0980.080
    TransUnet(100%)0.9320.9360.8750.8840.2030.3240.0880.104
    Swin-Unet(100%)0.9210.8870.8580.8050.9972.2590.1230.211
    Pred_noise(20%)0.9320.9420.8750.8930.1690.3430.0880.082
    Table 2. Comparison of experimental results using different methods
    TDDice(↑)JJM(↑)HHD(↓)PPAD(↓)
    LumenMediaLumenMediaLumenMediaLumenMedia
    10.9190.9210.8550.8600.3180.4820.1210.139
    1000.9270.9390.8660.8880.1240.1810.0930.074
    2000.9320.9420.8750.8930.1690.3430.0880.082
    5000.9260.9410.8650.8910.1690.3270.0880.084
    10000.9200.9360.8560.8830.3670.4510.1170.094
    Table 3. Comparison of results of different noise level numbers
    Loss functionDDice(↑)JJM(↑)HHD(↓)PPAD(↓)
    LumenMediaLumenMediaLumenMediaLumenMedia
    LMSE0.9270.9370.8660.8840.1970.3060.0880.087
    LMSE+0.1LSSIM0.9320.9420.8750.8930.1690.3430.0880.082
    Table 4. Comparison of results of different loss functions
    Wenyue Hao, Huaiyu Cai, Tingtao Zuo, Zhongwei Jia, Yi Wang, Xiaodong Chen. Self-Supervised Pre-Training for Intravascular Ultrasound Image Segmentation Method Based on Diffusion Model[J]. Laser & Optoelectronics Progress, 2024, 61(18): 1837005
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