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基于编码解码结构的微血管减压图像实时语义分割

白瑞峰 江山 孙海江 刘心睿

白瑞峰, 江山, 孙海江, 刘心睿. 基于编码解码结构的微血管减压图像实时语义分割[J]. 中国光学(中英文), 2022, 15(5): 1055-1065. doi: 10.37188/CO.2022-0120
引用本文: 白瑞峰, 江山, 孙海江, 刘心睿. 基于编码解码结构的微血管减压图像实时语义分割[J]. 中国光学(中英文), 2022, 15(5): 1055-1065. doi: 10.37188/CO.2022-0120
BAI Rui-feng, JIANG Shan, SUN Hai-jiang, LIU Xin-rui. Real-time semantic segmentation of microvascular decompression images based on encoder-decoder structure[J]. Chinese Optics, 2022, 15(5): 1055-1065. doi: 10.37188/CO.2022-0120
Citation: BAI Rui-feng, JIANG Shan, SUN Hai-jiang, LIU Xin-rui. Real-time semantic segmentation of microvascular decompression images based on encoder-decoder structure[J]. Chinese Optics, 2022, 15(5): 1055-1065. doi: 10.37188/CO.2022-0120

基于编码解码结构的微血管减压图像实时语义分割

doi: 10.37188/CO.2022-0120
基金项目: 吉林省科技发展计划项目(No. 20200404155YY,No. 20200401091GX);白求恩医学工程与仪器中心(长春)项目(No. Bqegczx2019047)
详细信息
    作者简介:

    白瑞峰(1994—),男,甘肃通渭人,博士研究生,2017年于兰州交通大学获得学士学位,主要从事智能医学图像处理方面的研究。E-mail: bairuifeng_ucas@126.com

    江 山(1986—),男,吉林长春人,副研究员,硕士生导师,2010年、2013年于吉林大学分别获得学士、硕士学位,主要从事深度学习、高速目标跟踪处理方面的研究。E-mail: 617798169@qq.com

    孙海江(1980—),男,吉林辉南人,研究员,博士生导师,2012年于中科院长春光机所获得博士学位,主要从事目标识别与跟踪技术及高清视频图像增强显示方面的研究。E-mail: sunhaijiang@126.com

    刘心睿(1980—),男,吉林长春人,副教授,副主任医师,硕士生导师,2006年于吉林大学获得临床医学硕士学位,2018年于吉林大学获得神经外科博士学位,主要从事显微镜及内镜下复杂颅底入路手术、术中磁共振引导神经系统肿瘤的外科治疗、脑积水脑脊液循环重建、脑神经网络与脑功能研究。E-mail: liuxinr@jlu.edu.cn

  • 中图分类号: TP394.1;TH691.9

Real-time semantic segmentation of microvascular decompression images based on encoder-decoder structure

Funds: Supported by Jilin Province Science and Technology Development Plan Project (No. 20200404155YY, No. 20200401091GX); Bethune Center for Medical Engineering and Instrumentation (Changchun) (No. BQEGCZX2019047)
More Information
  • 摘要:

    针对真彩色微血管减压图像实时语义分割网络参数量大、语义分割精度低的问题,本文提出了一种适用于微血管减压场景的U型轻量级快速语义分割网络U-MVDNet (U-Shaped Microvascular Decompression Network),该网络由编码解码结构构成。在编码器中设计了轻型非对称瓶颈模块(LABM)对上下文特征进行编码,解码器中引入了特征融合模块(FFM),有效组合高级语义特征和低级空间细节。实验结果表明:对于微血管减压测试集,U-MVDNet在单NVIDIA GTX 2080Ti上的参数量只有0.66 M,平均交并比(mIoU)达到了76.29%,速度达到140 frame/s,且当输入图像尺寸为$640 \times 480$时,U-MVDNet在嵌入式平台 NVIDIA Jetson AGX Xavier上实现了实时(24 frame/s)语义分割。本文方法未使用任何的预训练模型,参数量少且推理速度快,语义分割性能优于其他对比方法,在分割精度和速度上做到了良好的平衡。同时,还可以方便地在嵌入式平台上开发和应用,性能优越,易于部署。

     

  • 图 1  U-MVDNet架构

    Figure 1.  Architecture of U-MVDNet

    图 2  (a)ResNet 瓶颈设计及(b)轻型非对称瓶颈模块

    Figure 2.  (a) ResNet bottleneck design and (b) LABM

    图 3  特征融合模块流程图

    Figure 3.  Flow chart of feature fusion module

    图 4  损失曲线图

    Figure 4.  Loss curves

    图 5  MVD验证集上的可视化对比结果

    Figure 5.  The visual comparison results of different methods on MVD validate set

    图 6  ISIC 2016 + PH2测试集上的可视化对比

    Figure 6.  The visual comparison results of different methods on ISIC 2016 + PH2 test set

    表  1  U-MVDNet架构细节

    Table  1.   Architecture details of proposed U-MVDNet

    LayerOperatorModeChannelOutput size
    1$3 \times 3$ Convstride 232$256 \times 256$
    2$3 \times 3$ Convstride 132$256 \times 256$
    3$3 \times 3$ Convstride 132$256 \times 256$
    4-5$n \times $LABMdilated 232$256 \times 256$
    6$3 \times 3$ Convstride 264$128 \times 128$
    7-8$m \times $LABMdilated 464$128 \times 128$
    9$3 \times 3$ Convstride 2128$64 \times 64$
    10-12$l \times $LABMdilated 8128$64 \times 64$
    131×FFM128$64 \times 64$
    141×FFM64$128 \times 128$
    151×FFM32$256 \times 256$
    161×1 Convstride 110$256 \times 256$
    17Bilinear interpolation$ \times 2$10$512 \times 512$
    下载: 导出CSV

    表  2  医学术语缩写及对应颜色

    Table  2.   Abbreviations of medical terms and corresponding color

    简称全称对应颜色
    cn5三叉神经
    cn7面神经
    cn9舌咽神经
    cn10迷走神经
    aica+cn7小脑前下动脉及面神经
    pica+cn7小脑后下动脉及面神经
    pica小脑后下动脉
    aica小脑前下动脉
    pv岩静脉
    下载: 导出CSV

    表  3  训练参数

    Table  3.   Training parameters

    Parameter nameParameter selection
    Learning ratePolicyInitializationPower
    poly0.160.9
    OptimizerPolicyMomentumWeight decay
    SGD0.9$1\times10 ^{- 4}$
    Enter picture size$768 \times 576$
    Batch size8
    下载: 导出CSV

    表  4  不同扩张率组合的LABM编码器结果

    Table  4.   Results of LABM encoder with different combinations of dilation rates

    NameDilation ratesmIoU(%)
    LABM_N2M2L42,4,872.35
    LABM_N2M2L44,8,1672.08
    下载: 导出CSV

    表  5  不同设置下的LABM编码器结果

    Table  5.   Results of LABM encoder with different settings

    ConcatenationParams(M)FLOPs(G)mIoU(%)
    0.302.8172.35
    0.544.0373.08
    下载: 导出CSV

    表  6  输入尺寸为512 × 512时,不同深度的编码器结果

    Table  6.   Results of encoder with different depths when the input size is 512 × 512

    nmlParams(M)FLOPs(G)mIoU(%)
    2220.523.9572.35
    2240.544.0373.08
    2440.554.1173.84
    4440.554.2073.37
    下载: 导出CSV

    表  7  不同构成要素的FFM解码器结果

    Table  7.   Results of FFM decoder with different components

    FFMPoolingmIoU(%)
    w/o73.84
    w77.11
    w77.34
    下载: 导出CSV

    表  8  U-MVDNet的扩张率对mIoU的影响

    Table  8.   Effect of dilation of U-MVDNet on mIoU

    ConcatenationmIoU(%)Params(M)
    U-MVDNet77.340.66
    U-MVDNet_w/o dilation75.610.66
    U-MVDNet_First $3 \times 3$ conv ($r = 2$)76.810.66
    下载: 导出CSV

    表  9  MVD测试集实验结果

    Table  9.   Experimental results on MVD test set

    MethodParams(M)Speed(frame·s−1)mIoU(%)cn5cn7cn9cn10aica+cn7pica+cn7picaaicapv
    CGNet[28]0.9487.471.9581.2682.971.2969.8571.6487.1667.3765.6650.42
    EDANet[29]0.6912574.5183.0384.0270.3177.2575.0987.9870.3768.1854.34
    ContextNet[30]0.88163.375.8182.1484.1574.9178.0876.6787.8472.0869.7756.65
    U-MVDNet0.66140.876.2982.2585.4574.876.9176.3287.8574.0869.8359.12
    下载: 导出CSV

    表  10  ISIC 2016 + PH2测试集实验结果

    Table  10.   Experimental results on ISIC 2016 + PH2 test set

    ModelParams
    (M)
    Speed
    (frame·s−1)
    DIC
    (%)
    JAC
    (%)
    ACC
    (%)
    SPE
    (%)
    SEN
    (%)
    DeepLabv3[31]58.298.788.681.291.989.195.9
    CA-Net[32]2.79130.388.780.593.291.396.9
    U-MVDNet0.66175.189.381.793.293.394.3
    下载: 导出CSV

    表  11  两种不同的硬件环境

    Table  11.   Two different hardware environments

    Jetson Xavier服务器
    GPUVoltaGTX 2080Ti
    CPU8核Carmel ARM8核i7-9700K
    显存32GB LPDDR4x11GB GDDR6
    显存带宽136.5 GB/s616 GB/s
    CUDA核心5124352
    下载: 导出CSV

    表  12  不同分辨率下的测试结果

    Table  12.   Test results by different methods with different resolutions

    MethodSizeTimes/msSpeed/frame·s−1mIoU/%
    CGNet[28]$640 \times 480$65.715.270.31
    $768 \times 576$69.214.471.95
    EDANet[29]$640 \times 480$42.323.673.2
    $768 \times 576$45.222.174.18
    ContextNet[30]$640 \times 480$34.528.974.81
    $768 \times 576$36.127.775.81
    U-MVDNet$640 \times 480$41.524.275.76
    $768 \times 576$43.622.976.29
    下载: 导出CSV
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  • 收稿日期:  2022-06-10
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