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Boundary attention assisted dynamic graph convolution for retinal vascular segmentation
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摘要:
针对视网膜血管分割任务中存在的毛细血管分割遗漏和断连的问题，从最大限度地利用视网膜血管的特征信息的角度出发，添补视网膜血管的全局结构信息和边界信息，在U型网络的基础上，提出边界注意力辅助的动态图卷积视网膜血管分割网络。本模型先将动态图卷积嵌入到U型网络中形成多尺度结构，提升模型获取全局结构信息的能力，以提高分割质量，再利用边界注意力网络辅助模型，增加模型对边界信息的关注度，进一步提高分割性能。将模型在DRIVE、CHASEDB1和STARE三个视网膜图像数据集上进行实验，均取得了较好的分割效果。实验结果证明，该模型能较好地区分噪声和毛细血管，分割出结构较完整的视网膜血管，具有泛化性和鲁棒性。
Abstract:Aiming at the problem of missing and disconnected capillary segmentation in the retinal vascular segmentation task, from the perspective of maximizing the use of retinal vascular feature information, by adding the global structure information and retinal blood vessels boundary information, based on the U-shaped network, a dynamic graph convolution for retinal vascular segmentation model assisted by boundary attention is proposed. The dynamic graph convolution is first embedded into the U-shaped network to form a multi-scale structure, which improves the ability of the model to obtain the global structural information, and thus improving the segmentation quality. Then, the boundary attention network is utilized to assist the model to increase the attention to the boundary information, and further improve the segmentation performance. The proposed algorithm is tested on three retinal image datasets, DRIVE, CHASEDB1, and STARE, and good segmentation results are obtained. The experimental results show that the model can better distinguish the noise and capillary, and segment retinal blood vessels with more complete structure, which has generalization and robustness.
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图 1 边界注意力辅助的动图卷积U型网络
Figure 1. Boundary attention assisted dynamic graph convolution U-shaped network
图 3 动态图卷积计算过程和特征融合网络。(a) 动态图卷积计算过程；(b) 特征融合网络
Figure 3. Dynamic graph convolution calculation process and Feature fusion network. (a) Dynamic graph convolution calculation process; (b) Feature fusion network
图 5 数据预处理结果图。(a) 预处理图像切片；(b) 真实标注切片
Figure 5. Data preprocessing results. (a) Pre-processed image slices; (b) Ground truth slices
图 6 消融实验效果对比图。(a) 原图及原图细节；(b) 真实标注；(c) U-Net；(d) DGU-Net；(e) BU-Net；(f) BDGU-Net
Figure 6. Comparison of ablation results. (a) Original image and details; (b) Ground truth; (c) U-Net; (d) DGU-Net; (e) BU-Net; (f) BDGU-Net
图 7 不同网络效果对比图。(a) 原图及原图细节；(b) 真实标注；(c) Iternet；(d) MLA-DU-Net；(e) Res2Unet；(f) BDGU-Net
Figure 7. Comparison of ablation results. (a) Original image and details; (b) Ground truth; (c) Iternet; (d) MLA-DU-Net; (e) Res2Unet; (f) BDGU-Net
表 1 评价指标
Table 1. Evaluation indexes
F1 SE SP ACC $\dfrac{{2 \times PR \times SE}}{{PR + SE}}$ $\dfrac{{TP}}{{TP{\text{ + }}FN}}$ $\dfrac{{TN}}{{TN + FP}}$ $\dfrac{{TP + TN}}{{TP + TN + FP + FN}}$ 
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表 2 消融实验结果
Table 2. Ablation experiments results
Dataset Network F1 SE SP ACC AUC Time/s DRIVE U-Net 0.8353 0.8330 0.9777 0.9601 0.9848 1.00 DGU-Net 0.8357 0.8275 0.9789 0.9605 0.9850 1.22 BU-Net 0.8355 0.8352 0.9773 0.9600 0.9849 1.00 BDGU-Net 0.8359 0.8300 0.9785 0.9604 0.9851 1.24 CHASEDB1 U-Net 0.8108 0.8052 0.9820 0.9660 0.9854 1.31 DGU-Net 0.8053 0.7812 0.9842 0.9658 0.9846 1.73 BU-Net 0.8133 0.8210 0.9803 0.9659 0.9858 1.31 BDGU-Net 0.8136 0.8146 0.9813 0.9662 0.9856 1.76 STARE U-Net 0.7845 0.7380 0.9863 0.9648 0.9810 2.21 DGU-Net 0.7824 0.7045 0.9908 0.9660 0.9825 2.85 BU-Net 0.7845 0.7352 0.9868 0.9650 0.9806 2.21 BDGU-Net 0.7934 0.7273 0.9900 0.9672 0.9834 2.85
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表 3 不同α和β系数取值分析
Table 3. Value analysis of different α and β coefficients
α β F1 SE SP ACC AUC 0.8 0.2 0.8348 0.8252 0.9790 0.9604 0.9848 0.7 0.3 0.8351 0.8230 0.9795 0.9605 0.9848 0.6 0.4 0.8359 0.8300 0.9785 0.9604 0.9851 0.5 0.5 0.8351 0.8284 0.9785 0.9603 0.9849
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表 4 不同网络在DRIVE、CHASEDB1和STARE数据集的指标对比
Table 4. Index comparison of different networks in DRIVE, CHASEDB1 and STARE datasets
Dataset Network Year F1 SE SP ACC AUC Time/s DRIVE 2nd Human Observer[20] — — 0.7760 0.9724 0.9472 — — Iternet[21] 2020 0.8353 0.8370 0.9770 0.9600 0.9847 1.37 Yang[22] 2021 0.8297 0.8353 0.9751 0.9579 — — MLA-DU-Net[23] 2021 0.8352 0.8269 0.9788 0.9603 0.9849 1.64 Res2Unet[24] 2022 0.8292 0.8332 0.9756 0.9583 0.9782 1.21 Zhang[15] 2022 0.8349 0.8345 0.9773 0.9600 0.9850 1.20 BDGU-Net 2022 0.8359 0.8300 0.9785 0.9604 0.9851 1.24 CHASEDB1 2nd Human Observer[20] — — 0.8105 0.9711 0.9545 — — Iternet[21] 2020 0.8120 0.8144 0.9809 0.9658 0.9856 1.48 Yang[22] 2021 0.7997 0.8176 0.9776 0.9632 — — MLA-DU-Net[23] 2021 0.8063 0.8137 0.9796 0.9646 0.9841 2.13 Res2Unet[24] 2022 0.8071 0.8444 0.9753 0.9634 0.9794 1.47 Zhang[15] 2022 0.8050 0.8141 0.9792 0.9643 0.9841 1.72 BDGU-Net 2022 0.8136 0.8146 0.9813 0.9662 0.9856 1.76 STARE 2nd Human Observer[20] — — 0.8952 0.9384 0.9349 — — Iternet[21] 2020 0.7962 0.7490 0.9874 0.9667 0.9838 2.76 Yang[22] 2021 0.8155 0.7946 0.9821 0.9626 — — MLA-DU-Net[23] 2021 0.7965 0.7925 0.9812 0.9649 0.9826 3.95 Res2Unet[24] 2022 0.7858 0.7392 0.9865 0.9650 0.9709 2.77 Zhang[15] 2022 0.7878 0.7381 0.9871 0.9655 0.9823 3.07 BDGU-Net 2022 0.7934 0.7273 0.9900 0.9672 0.9834 2.85
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