Location and segmentation method of optic disc in fundus images based on deep learning_Chinese Journal of Ocular Fundus Diseases

Authors：

Wan Cheng ¹ , Zhou Xueting ¹ , Zhou Peng ¹ , Shen Jianxin ¹ ,  Yu Qiuli ²

1. College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Department of Ophthalmology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210003, China;

Corresponding author：

Yu Qiuli, Email: 15905192943@163.com

Keywords：

Neural networks (computer); Deep learning; Optic disk localization; Optic disk segment

DOI：

10.3760/cma.j.cn511434-20190712-00224

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To observe and analyze the accuracy of the optic disc positioning and segmentation method of fundus images based on deep learning.Methods The model training strategies were training and evaluating deep learning-based optic disc positioning and segmentation methods on the ORIGA dataset. A deep convolutional neural network (CNN) was built on the Caffe framework of deep learning. A sliding window was used to cut the original image of the ORIGA data set into many small pieces of pictures, and the deep CNN was used to determine whether each small piece of picture contained the complete disc structure, so as to find the area of the disc. In order to avoid the influence of blood vessels on the segmentation of the optic disc, the blood vessels in the optic disc area were removed before segmentation of the optic disc boundary. A deep network of optic disc segmentation based on image pixel classification was used to realize the segmentation of the optic disc of fundus images. The accuracy of the optic disc positioning and segmentation method was calculated based on deep learning of fundus images. Positioning accuracy=T/N, T represented the number of fundus images with correct optic disc positioning, and N represented the total number of fundus images used for positioning. The overlap error was used to compare the difference between the segmentation result of the optic disc and the actual boundary of the optic disc.Results On the dataset from ORIGA, the accuracy of the optic disc localization can reach 99.6%, the average overlap error of optic disc segmentation was 7.1%. The calculation errors of the average cup-to-disk ratio for glaucoma images and normal images were 0.066 and 0.049, respectively. Disc segmentation of each image took an average of 10 ms.Conclusion The algorithm can locate the disc area quickly and accurately, and can also segment the disc boundary more accurately.

Citation： Wan Cheng, Zhou Xueting, Zhou Peng, Shen Jianxin, Yu Qiuli. Location and segmentation method of optic disc in fundus images based on deep learning. Chinese Journal of Ocular Fundus Diseases, 2020, 36(8): 628-632. doi: 10.3760/cma.j.cn511434-20190712-00224 Copy

1.	Fu HZ, Cheng J, Xu YW, et al. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation[J]. IEEE Trans Med Imag, 2018, 37(7): 1597-1605. DOI: 10.1109/TMI.2018.2791488.
2.	Lu SJ, Lim JH. Automatic optic disc detection from retinal images by a line operator[J]. IEEE Trans Biomed Eng, 2011, 58(1): 88-94. DOI: 10.1109/TBME.2010.2086455.
3.	Zhang DB, Zhao YY. Novel accurate and fast optic disc detection in retinal images with vessel distribution and directional characteristics[J]. IEEE J Biomed Health Inform, 2015, 20(1): 333-342. DOI: 10.1109/JBHI.2014.2365514.
4.	Cheng J, Liu J, Wong DW, et al. Automatic optic disc segmentation with peripapillary atrophy elimination[C]. International Conference Proceedings of IEEE Engineering in Medicine and Biology Society, 2011: 6224-6227. DOI: 10.1109/IEMBS.2011.6091537.
5.	Joshi GD, Sivaswamy J, Krishnadas SR. Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment[J]. IEEE Trans Med Imag, 2011, 30(6): 1192-1205. DOI: 10.1109/TMI.2011.2106509.
6.	Yin F, Liu J, Ong S H, et al. Model-based optic nerve head segmentation on retinal fundus images[C]. Engineering in Medicine and Biology Society, 2011: 2626-2629. DOI: 10.1109/IEMBS.2011.6090724.
7.	Tang L, Garvin MK, Kwon YH, et al. Segmentation of optic nerve head rim in color fundus photographs by probability based active shape model[J]. Invest Ophthalmol Vis Sci, 2012, 53(14): 2144.
8.	Cheng J, Liu J, Xu YW, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening[J]. IEEE Trans Med Imag, 2013, 32(6): 1019-1032. DOI: 10.1109/TMI.2013.2247770.
9.	Lu HT, Zhang QC. Applications of deep convolutional neural network in computer vision[J]. J Data Acquisition Process, 2016, 31(1): 1-17. DOI: 10.16337/j.1004-9037.2016.01.001.
10.	童妍, 卢苇, 邢怡桥, 等. 人工智能在眼科诊断中的应用研究现状[J]. 中华眼底病杂志, 2019, 35(5): 506-509. DOI: 10.3760/cma.j.issn.1005-1015.2019.05.019.Tong Y, Lu W, Xing YQ, et al. Applications of artificial intelligence in the diagnosis of eye diseases[J]. Chin J Ocul Fundus Dis, 2019, 35(5): 506-509. DOI: 10.3760/cma.j.issn.1005-1015.2019.05.019.
11.	陈有信, 张碧磊, 张弘哲, 等. 眼科人工智能技术的现状与问题[J]. 中华眼底病杂志, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.Chen YX, Zhang BL, Zhang HZ, et al. Insights and prospectives of ophthalmologic artificial intelligence technology[J]. Chin J Ocul Fundus Dis, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.
12.	Esedoglu S, Shen J. Digital in painting based on the Mumford-Shah-Euler image model[J]. Eur J Appl Math, 2002, 13(4): 353-370. DOI: 10.1017/S0956792502004904.
13.	Liang JL, Zhang MH, Liu D, et al. Robust ellipse fitting based on sparse combination of data points[J]. IEEE Trans Image Process, 2013, 22(6): 2207-2218. DOI: 10.1109/TIP.2013.2246518.
14.	Zhang Z, Yin FS, Liu J, et al. ORIGA-light: an online retinal fundus image database for glaucoma analysis and research[C]. International Conference of IEEE Engineering in Medicine & Biology Society, 2010: 3065-3068. DOI: 10.1109/IEMBS.2010.5626137.
15.	Xu J, Chutatape O, Sung E, et al. Optic disk feature extraction via modified deformable model technique for glaucoma analysis[J]. Pattern Recognition, 2007, 40(7): 2063-2076. DOI: 10.1016/j.patcog.2006.10.015.
16.	Cheng J, Liu J, Xu Y, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening[J]. IEEE Transactions on Medical Imaging, 2013, 32(6): 1019-1032. DOI: 10.1109/TMI.2013.2247770.
17.	Xu Y, Xu D, Lin S, et al. Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis[C]. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2011, 14: 1-8. DOI: 10.1007/978-3-642-23626-6_1.

1. Fu HZ, Cheng J, Xu YW, et al. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation[J]. IEEE Trans Med Imag, 2018, 37(7): 1597-1605. DOI: 10.1109/TMI.2018.2791488.
2. Lu SJ, Lim JH. Automatic optic disc detection from retinal images by a line operator[J]. IEEE Trans Biomed Eng, 2011, 58(1): 88-94. DOI: 10.1109/TBME.2010.2086455.
3. Zhang DB, Zhao YY. Novel accurate and fast optic disc detection in retinal images with vessel distribution and directional characteristics[J]. IEEE J Biomed Health Inform, 2015, 20(1): 333-342. DOI: 10.1109/JBHI.2014.2365514.
4. Cheng J, Liu J, Wong DW, et al. Automatic optic disc segmentation with peripapillary atrophy elimination[C]. International Conference Proceedings of IEEE Engineering in Medicine and Biology Society, 2011: 6224-6227. DOI: 10.1109/IEMBS.2011.6091537.
5. Joshi GD, Sivaswamy J, Krishnadas SR. Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment[J]. IEEE Trans Med Imag, 2011, 30(6): 1192-1205. DOI: 10.1109/TMI.2011.2106509.
6. Yin F, Liu J, Ong S H, et al. Model-based optic nerve head segmentation on retinal fundus images[C]. Engineering in Medicine and Biology Society, 2011: 2626-2629. DOI: 10.1109/IEMBS.2011.6090724.
7. Tang L, Garvin MK, Kwon YH, et al. Segmentation of optic nerve head rim in color fundus photographs by probability based active shape model[J]. Invest Ophthalmol Vis Sci, 2012, 53(14): 2144.
8. Cheng J, Liu J, Xu YW, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening[J]. IEEE Trans Med Imag, 2013, 32(6): 1019-1032. DOI: 10.1109/TMI.2013.2247770.
9. Lu HT, Zhang QC. Applications of deep convolutional neural network in computer vision[J]. J Data Acquisition Process, 2016, 31(1): 1-17. DOI: 10.16337/j.1004-9037.2016.01.001.
10. 童妍, 卢苇, 邢怡桥, 等. 人工智能在眼科诊断中的应用研究现状[J]. 中华眼底病杂志, 2019, 35(5): 506-509. DOI: 10.3760/cma.j.issn.1005-1015.2019.05.019.Tong Y, Lu W, Xing YQ, et al. Applications of artificial intelligence in the diagnosis of eye diseases[J]. Chin J Ocul Fundus Dis, 2019, 35(5): 506-509. DOI: 10.3760/cma.j.issn.1005-1015.2019.05.019.
11. 陈有信, 张碧磊, 张弘哲, 等. 眼科人工智能技术的现状与问题[J]. 中华眼底病杂志, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.Chen YX, Zhang BL, Zhang HZ, et al. Insights and prospectives of ophthalmologic artificial intelligence technology[J]. Chin J Ocul Fundus Dis, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.
12. Esedoglu S, Shen J. Digital in painting based on the Mumford-Shah-Euler image model[J]. Eur J Appl Math, 2002, 13(4): 353-370. DOI: 10.1017/S0956792502004904.
13. Liang JL, Zhang MH, Liu D, et al. Robust ellipse fitting based on sparse combination of data points[J]. IEEE Trans Image Process, 2013, 22(6): 2207-2218. DOI: 10.1109/TIP.2013.2246518.
14. Zhang Z, Yin FS, Liu J, et al. ORIGA-light: an online retinal fundus image database for glaucoma analysis and research[C]. International Conference of IEEE Engineering in Medicine & Biology Society, 2010: 3065-3068. DOI: 10.1109/IEMBS.2010.5626137.
15. Xu J, Chutatape O, Sung E, et al. Optic disk feature extraction via modified deformable model technique for glaucoma analysis[J]. Pattern Recognition, 2007, 40(7): 2063-2076. DOI: 10.1016/j.patcog.2006.10.015.
16. Cheng J, Liu J, Xu Y, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening[J]. IEEE Transactions on Medical Imaging, 2013, 32(6): 1019-1032. DOI: 10.1109/TMI.2013.2247770.
17. Xu Y, Xu D, Lin S, et al. Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis[C]. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2011, 14: 1-8. DOI: 10.1007/978-3-642-23626-6_1.

Chinese Journal of Ocular Fundus Diseases

Location and segmentation method of optic disc in fundus images based on deep learning

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content