Real-time semantic segmentation of microvascular decompression images based on encoder-decoder structure
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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。Abstract:Aiming at the problems of large parameters and low semantic segmentation accuracy of real-time semantic segmentation networks for true-color microvascular decompression (MVD) images. This paper proposes a U-shaped lightweight fast semantic segmentation network U-MVDNet (U-Shaped Microvascular Decompression Network) for MVD scenarios, which consists of encoder-decoder structure. A Light Asymmetric Bottleneck Module (LABM) is designed in the encoder to encode context features. Feature Fusion Module (FFM) is introduced in the decoder to effectively combine high-level semantic features and underlying spatial details. Experimental results show that for the MVD test set, U-MVDNet achieves 0.66 M parameters, 76.29% mIoU (mean Intersection-over-Union), and 140 frame/s speed on NVIDIA GTX 2080Ti. And when input image size is 640 × 480, the real-time (24 frame/s) semantic segmentation is realized on NVIDIA Jetson AGX Xavier embedded development board. The proposed network has no pretrained model, fewer parameters, and fast inference speed. The semantic segmentation performance is superior to other comparison methods, and a good trade-off between segmentation accuracy and speed is achieved. Furthermore, U-MVDNet can also be easily developed and applied on embedded platform with superior performance and easy deployment.
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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
Layer Operator Mode Channel Output size 1 $3 \times 3$ Conv stride 2 32 $256 \times 256$ 2 $3 \times 3$ Conv stride 1 32 $256 \times 256$ 3 $3 \times 3$ Conv stride 1 32 $256 \times 256$ 4-5 $n \times $LABM dilated 2 32 $256 \times 256$ 6 $3 \times 3$ Conv stride 2 64 $128 \times 128$ 7-8 $m \times $LABM dilated 4 64 $128 \times 128$ 9 $3 \times 3$ Conv stride 2 128 $64 \times 64$ 10-12 $l \times $LABM dilated 8 128 $64 \times 64$ 13 1×FFM − 128 $64 \times 64$ 14 1×FFM − 64 $128 \times 128$ 15 1×FFM − 32 $256 \times 256$ 16 1×1 Conv stride 1 10 $256 \times 256$ 17 Bilinear interpolation $ \times 2$ 10 $512 \times 512$ 
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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 
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表 3 训练参数
Table 3. Training parameters
Parameter name Parameter selection Learning rate Policy Initialization Power poly 0.16 0.9 Optimizer Policy Momentum Weight decay SGD 0.9 $1\times10 ^{- 4}$ Enter picture size $768 \times 576$ Batch size 8
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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
Name Dilation rates mIoU(%) LABM_N2M2L4 2,4,8 72.35 LABM_N2M2L4 4,8,16 72.08
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表 5 不同设置下的LABM编码器结果
Table 5. Results of LABM encoder with different settings
Concatenation Params(M) FLOPs(G) mIoU(%) 0.30 2.81 72.35 √ 0.54 4.03 73.08
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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
n m l Params(M) FLOPs(G) mIoU(%) 2 2 2 0.52 3.95 72.35 2 2 4 0.54 4.03 73.08 2 4 4 0.55 4.11 73.84 4 4 4 0.55 4.20 73.37
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表 7 不同构成要素的FFM解码器结果
Table 7. Results of FFM decoder with different components
FFM Pooling mIoU(%) w/o − 73.84 w 77.11 w √ 77.34
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表 8 U-MVDNet的扩张率对mIoU的影响
Table 8. Effect of dilation of U-MVDNet on mIoU
Concatenation mIoU(%) Params(M) U-MVDNet 77.34 0.66 U-MVDNet_w/o dilation 75.61 0.66 U-MVDNet_First $3 \times 3$ conv ($r = 2$) 76.81 0.66
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表 9 MVD测试集实验结果
Table 9. Experimental results on MVD test set
Method Params(M) Speed(frame·s−1) mIoU(%) cn5 cn7 cn9 cn10 aica+cn7 pica+cn7 pica aica pv CGNet[28] 0.94 87.4 71.95 81.26 82.9 71.29 69.85 71.64 87.16 67.37 65.66 50.42 EDANet[29] 0.69 125 74.51 83.03 84.02 70.31 77.25 75.09 87.98 70.37 68.18 54.34 ContextNet[30] 0.88 163.3 75.81 82.14 84.15 74.91 78.08 76.67 87.84 72.08 69.77 56.65 U-MVDNet 0.66 140.8 76.29 82.25 85.45 74.8 76.91 76.32 87.85 74.08 69.83 59.12
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表 11 两种不同的硬件环境
Table 11. Two different hardware environments
Jetson Xavier 服务器 GPU Volta GTX 2080Ti CPU 8核Carmel ARM 8核i7-9700K 显存 32GB LPDDR4x 11GB GDDR6 显存带宽 136.5 GB/s 616 GB/s CUDA核心 512 4352
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表 12 不同分辨率下的测试结果
Table 12. Test results by different methods with different resolutions
Method Size Times/ms Speed/frame·s−1 mIoU/% CGNet[28] $640 \times 480$ 65.7 15.2 70.31 $768 \times 576$ 69.2 14.4 71.95 EDANet[29] $640 \times 480$ 42.3 23.6 73.2 $768 \times 576$ 45.2 22.1 74.18 ContextNet[30] $640 \times 480$ 34.5 28.9 74.81 $768 \times 576$ 36.1 27.7 75.81 U-MVDNet $640 \times 480$ 41.5 24.2 75.76 $768 \times 576$ 43.6 22.9 76.29
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