Optical Instruments, Volume. 46, Issue 5, 9(2024)
Retinal blood vessel segmentation algorithm based on improved U-Net
Figures & Tables(11)
Fig. 6. Line chart for comparison of different algorithm indicators
Table 1. Experimental parameter configuration
View tableView in ArticleTable 1. Experimental parameter configuration
名称 参数配置 优化器 Adam 初始学习率 0.001 训练步数 200 批处理大小 4
Table 2. Performance indicators
View tableView in ArticleTable 2. Performance indicators
名称 缩写 公式 含义 敏感性 Se $ {\mathrm{Se}}=\dfrac{{\mathrm{TP}}}{{\mathrm{TP}}+{\mathrm{FN}}} $ 血管被正确分割的指数 特异性 Sp $ {\mathrm{Sp}}=\dfrac{{\mathrm{TN}}}{{\mathrm{TN}}+{\mathrm{FP}}} $ 图像背景被正确分割的指数 准确性 Acc $ {\mathrm{Acc}}=\dfrac{{\mathrm{TP}}+{\mathrm{TN}}}{{\mathrm{TP}}+{\mathrm{TN}}+{\mathrm{FP}}+{\mathrm{FN}}} $ 图像整体被正确分割的指数 F1值 F1 ${\mathrm{ F}}1=\dfrac{{\mathrm{2TP}}}{{\mathrm{2TP}}+{\mathrm{FP}}+{\mathrm{FN}}} $ 衡量分割结果和标准结果之间相似性的指数
Table 3. Results of different structural models based on U-Net
View tableView in ArticleTable 3. Results of different structural models based on U-Net
Algorithm Acc Se Sp F1 U-Net 0.9627 0.8159 0.9806 0.8137 U-Net+Res 0.9649 0.8168 0.9859 0.8192 U-Net+DEA 0.9659 0.8356 0.9831 0.8254 Our algorithm 0.9673 0.8373 0.9843 0.8293
Table 4. Results of different algorithms on DRIVE
View tableView in ArticleTable 4. Results of different algorithms on DRIVE
Method Acc Se Sp F1 AG-Net[ 18 ]0.9654 0.7638 0.9739 0.8132 CE-Net[ 19 ]0.9631 0.7746 0.9761 0.8156 DUNet[ 20 ]0.9579 0.7439 0.9872 0.8265 SCS-Net[ 21 ]0.9768 0.8157 0.9721 0.8241 CA-Net[ 22 ]0.9617 0.8042 0.9743 0.8104 ASU-Net[ 23 ]0.9678 0.8193 0.9789 0.8183 PVT-CASCADE[ 24 ]0.9736 0.8265 0.9773 0.8231 UNet-2022[ 25 ]0.9628 0.8073 0.9812 0.8117 Our method 0.9673 0.8373 0.9843 0.8293
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Yuan LIU, Baicheng LI, Chunbo WU. Retinal blood vessel segmentation algorithm based on improved U-Net[J]. Optical Instruments, 2024, 46(5): 9
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Received: Aug. 28, 2023
Accepted: --
Published Online: Jan. 3, 2025
The Author Email: LI Baicheng (lbcusst@163.com)
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