Segmentation of retinal vessels by fusing contour information and conditional generative adversarial_Journal of Biomedical Engineering

Authors：

 LIANG Liming ¹ , LAN Zhimin ¹ , SHENG Xiaoqi ¹ , XIE Zhaoben ² , LIU Wanrong ³

1. School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, P.R.China;
2. Department of Information Engineering, Gannan Medical University, Ganzhou, Jiangxi 341000, P.R.China;
3. Department of Ophthalmology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi 341000, P.R.China;

Corresponding author：

LIANG Liming, Email: lianglm67@163.com

Keywords：

contour loss function; depth-wise separable convolutions; squeeze-and-exception blocks; conditional generative adversarial nets; retinal vessel segmentation

DOI：

10.7507/1001-5515.202005019

Video：

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The existing retinal vessels segmentation algorithms have various problems that the end of main vessels are easy to break, and the central macula and the optic disc boundary are likely to be mistakenly segmented. To solve the above problems, a novel retinal vessels segmentation algorithm is proposed in this paper. The algorithm merged together vessels contour information and conditional generative adversarial nets. Firstly, non-uniform light removal and principal component analysis were used to process the fundus images. Therefore, it enhanced the contrast between the blood vessels and the background, and obtained the single-scale gray images with rich feature information. Secondly, the dense blocks integrated with the deep separable convolution with offset and squeeze-and-exception (SE) block were applied to the encoder and decoder to alleviate the gradient disappearance or explosion. Simultaneously, the network focused on the feature information of the learning target. Thirdly, the contour loss function was added to improve the identification ability of the blood vessels information and contour information of the network. Finally, experiments were carried out on the DRIVE and STARE datasets respectively. The value of area under the receiver operating characteristic reached 0.982 5 and 0.987 4, respectively, and the accuracy reached 0.967 7 and 0.975 6, respectively. Experimental results show that the algorithm can accurately distinguish contours and blood vessels, and reduce blood vessel rupture. The algorithm has certain application value in the diagnosis of clinical ophthalmic diseases.

Citation： LIANG Liming, LAN Zhimin, SHENG Xiaoqi, XIE Zhaoben, LIU Wanrong. Segmentation of retinal vessels by fusing contour information and conditional generative adversarial. Journal of Biomedical Engineering, 2021, 38(2): 276-285. doi: 10.7507/1001-5515.202005019 Copy

1.	Zhao Yuqian, Wang Xiaohong, Wang Xiaofang, et al. Retinal vessels segmentation based on level set and region growing. Pattern Recognit, 2014, 47(7): 2437-2446.
2.	梁礼明, 黄朝林, 石霏, 等. 融合形状先验的水平集眼底图像血管分割. 计算机学报, 2018, 41(7): 1678-1692.
3.	Yin Y, Adel M, Bourennane S. Retinal vessel segmentation using a probabilistic tracking method. Pattern Recognit, 2012, 45(4): 1235-1244.
4.	Nguyen U T V, Bhuiyan A, Park L A F, et al. An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognit, 2013, 46(3): 703-715.
5.	Zhu Chengzhang, Zou Beiji, Zhao Rongchang, et al. Retinal vessel segmentation in colour fundus images using extreme learning machine. Comput Med Imaging Graph, 2017, 55: 68-77.
6.	Memari N, Ramli A R, Bin S M I, et al. Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier. PloS One, 2017, 12(12): 1-35.
7.	梁礼明, 刘博文, 杨海龙, 等. 基于多特征融合的有监督视网膜血管提取. 计算机学报, 2018, 41(11): 2566-2580.
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11.	梁礼明, 盛校棋, 蓝智敏, 等. 自适应尺度信息的U型视网膜血管分割算法. 光学学报, 2019, 39(8): 126-140.
12.	Soomro T A, Afifi A J, Gao J B, et al. Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation. Expert Syst Appl, 2019, 134(134): 36-52.
13.	Son J, Park S J, Jung K H. Towards accurate segmentation of retinal vessels and the optic disc in fundoscopic images with generative adversarial networks. J Digit Imaging, 2019, 32(3): 499-512.
14.	Staal J, Abramoff M D, Niemeijer M, et al. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging, 2004, 23(4): 501-509.
15.	Hoover A D, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging, 2000, 19(3): 203-210.
16.	Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets// Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal: MIT Press, 2014: 2672-2680.
17.	蒋芸, 谭宁. 基于条件深度卷积生成对抗网络的视网膜血管分割. 自动化学报, 2021, 47(1): 136-147.
18.	Sandler M, Howard A, Zhu M, et al. MobileNetV2: inverted residuals and linear bottlenecks// The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Utah: IEEE, 2018: 4510-4520.
19.	Chollet F. Xception: Deep Learning with depthwise separable convolutions// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Puerto Rico: IEEE, 2017: 1800-1807.
20.	Guo Jianbo, Li Yuxi, Lin Weiyao, et al. Network decoupling: from regular to depthwise separable convolutions. arXiv preprint arXiv, 2018: 1808.05517.
21.	Huang Gao, Liu Zhuang, Maaten L V D, et al. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2261-2269.
22.	He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Identity mappings in deep residual networks// Proceedings of the European Conference on Computer Vision (ECCV). Amsterdam: Springer, 2016: 630-645.
23.	Hu Jie, Shen Li, Albanie S, et al. Squeeze-and-excitation networks// The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Utah: IEEE, 2018: 7132-7141.
24.	Zhou Qing, Zhou Zhiyong, Chen Chunmiao, et al. Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images. Comput Biol Med, 2019, 4(107): 47-57.
25.	Yang Yibo, Zhong Zhisheng, Shen Tiancheng, et al. Convolutional neural networks with alternately updated clique// The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Utah: IEEE, 2018: 1-10.
26.	Ghiasi G, Lin T Y, Le Q V. DropBlock: a regularization method for convolutional networks. arXiv preprint arXiv, 2018: 1810.12890.
27.	Schlemper J, Oktay O, Chen L, et al. Attention-gated networks for improving ultrasound scan plane detection. arXiv preprint arXiv, 2018: 1804.05338.
28.	Isola P, Zhu J-Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks// The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Puerto Rico: IEEE, 2017: 5967-5976.
29.	Yang Hongju, Li Yao, Yan Xuefeng, et al. ContourGAN: image contour detection with generative adversarial network. Knowledge-Based Syst, 2019, 164(1): 21-28.
30.	Maninis K K, Pont-Tuset J, Arbelaez P, et al. Deep retinal image understanding. arXiv preprint arXiv, 2016: 1609.01103.
31.	Yan Zengqiang, Yang Xin, Cheng K T. Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans Biomed Eng, 2018, 65(9): 1912-1923.
32.	Yang Tiejun, Wu Tingting, Li Lei, et al. SUD-GAN: Deep convolution generative adversarial network combined with short connection and dense block for retinal vessel segmentation. J Digit Imaging, 2020, 33: 946-957.

1. Zhao Yuqian, Wang Xiaohong, Wang Xiaofang, et al. Retinal vessels segmentation based on level set and region growing. Pattern Recognit, 2014, 47(7): 2437-2446.
2. 梁礼明, 黄朝林, 石霏, 等. 融合形状先验的水平集眼底图像血管分割. 计算机学报, 2018, 41(7): 1678-1692.
3. Yin Y, Adel M, Bourennane S. Retinal vessel segmentation using a probabilistic tracking method. Pattern Recognit, 2012, 45(4): 1235-1244.
4. Nguyen U T V, Bhuiyan A, Park L A F, et al. An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognit, 2013, 46(3): 703-715.
5. Zhu Chengzhang, Zou Beiji, Zhao Rongchang, et al. Retinal vessel segmentation in colour fundus images using extreme learning machine. Comput Med Imaging Graph, 2017, 55: 68-77.
6. Memari N, Ramli A R, Bin S M I, et al. Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier. PloS One, 2017, 12(12): 1-35.
7. 梁礼明, 刘博文, 杨海龙, 等. 基于多特征融合的有监督视网膜血管提取. 计算机学报, 2018, 41(11): 2566-2580.
8. Ganin Y, Lempitsky V. Nˆ4-Fields: neural network nearest neighbor fields for image transforms// Proceedings of the 12th Asian Conference on Computer Vision (ACCV). Singapore: Springer, 2014: 536-551.
9. Yang J, Price B L, Cohen S, et al. Object contour detection with a fully convolutional encoder-decoder network//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 193-202.
10. Xie S, Tu Z. Holistically-nested edge detection. Int J Comput Vis, 2015, 125(1-3): 3-18.
11. 梁礼明, 盛校棋, 蓝智敏, 等. 自适应尺度信息的U型视网膜血管分割算法. 光学学报, 2019, 39(8): 126-140.
12. Soomro T A, Afifi A J, Gao J B, et al. Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation. Expert Syst Appl, 2019, 134(134): 36-52.
13. Son J, Park S J, Jung K H. Towards accurate segmentation of retinal vessels and the optic disc in fundoscopic images with generative adversarial networks. J Digit Imaging, 2019, 32(3): 499-512.
14. Staal J, Abramoff M D, Niemeijer M, et al. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging, 2004, 23(4): 501-509.
15. Hoover A D, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging, 2000, 19(3): 203-210.
16. Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets// Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal: MIT Press, 2014: 2672-2680.
17. 蒋芸, 谭宁. 基于条件深度卷积生成对抗网络的视网膜血管分割. 自动化学报, 2021, 47(1): 136-147.
18. Sandler M, Howard A, Zhu M, et al. MobileNetV2: inverted residuals and linear bottlenecks// The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Utah: IEEE, 2018: 4510-4520.
19. Chollet F. Xception: Deep Learning with depthwise separable convolutions// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Puerto Rico: IEEE, 2017: 1800-1807.
20. Guo Jianbo, Li Yuxi, Lin Weiyao, et al. Network decoupling: from regular to depthwise separable convolutions. arXiv preprint arXiv, 2018: 1808.05517.
21. Huang Gao, Liu Zhuang, Maaten L V D, et al. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2261-2269.
22. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Identity mappings in deep residual networks// Proceedings of the European Conference on Computer Vision (ECCV). Amsterdam: Springer, 2016: 630-645.
23. Hu Jie, Shen Li, Albanie S, et al. Squeeze-and-excitation networks// The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Utah: IEEE, 2018: 7132-7141.
24. Zhou Qing, Zhou Zhiyong, Chen Chunmiao, et al. Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images. Comput Biol Med, 2019, 4(107): 47-57.
25. Yang Yibo, Zhong Zhisheng, Shen Tiancheng, et al. Convolutional neural networks with alternately updated clique// The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Utah: IEEE, 2018: 1-10.
26. Ghiasi G, Lin T Y, Le Q V. DropBlock: a regularization method for convolutional networks. arXiv preprint arXiv, 2018: 1810.12890.
27. Schlemper J, Oktay O, Chen L, et al. Attention-gated networks for improving ultrasound scan plane detection. arXiv preprint arXiv, 2018: 1804.05338.
28. Isola P, Zhu J-Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks// The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Puerto Rico: IEEE, 2017: 5967-5976.
29. Yang Hongju, Li Yao, Yan Xuefeng, et al. ContourGAN: image contour detection with generative adversarial network. Knowledge-Based Syst, 2019, 164(1): 21-28.
30. Maninis K K, Pont-Tuset J, Arbelaez P, et al. Deep retinal image understanding. arXiv preprint arXiv, 2016: 1609.01103.
31. Yan Zengqiang, Yang Xin, Cheng K T. Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans Biomed Eng, 2018, 65(9): 1912-1923.
32. Yang Tiejun, Wu Tingting, Li Lei, et al. SUD-GAN: Deep convolution generative adversarial network combined with short connection and dense block for retinal vessel segmentation. J Digit Imaging, 2020, 33: 946-957.

Journal of Biomedical Engineering

Segmentation of retinal vessels by fusing contour information and conditional generative adversarial

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