937
Views
4
CrossRef citations to date
0
Altmetric
Review

Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review

, ORCID Icon, , ORCID Icon, ORCID Icon, & ORCID Icon show all
Pages 747-764 | Published online: 11 Mar 2022

References

  • World Health Organization (WHO). Blindness and vision impairment (2021). Available from: https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment. Accessed February, 2021.
  • Quigley HA. Number of people with glaucoma worldwide. Br J Ophthalmol. 1996;80(5):389–393. doi:10.1136/bjo.80.5.389
  • Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006;90(3):262–267. doi:10.1136/bjo.2005.081224
  • Eid TM, el-Hawary I, el-Menawy W. Prevalence of glaucoma types and legal blindness from glaucoma in the western region of Saudi Arabia: a hospital-based study. Int Ophthalmol. 2009;29(6):477–483. doi:10.1007/s10792-008-9269-4
  • Al obeidan SA, Dewedar A, Osman EA, Mousa A. The profile of glaucoma in a Tertiary Ophthalmic University Center in Riyadh, Saudi Arabia. Saudi J Ophthalmol. 2011;25(4):373–379. doi:10.1016/j.sjopt.2011.09.001
  • Day AC, Baio G, Gazzard G, et al. The prevalence of primary angle closure glaucoma in European derived populations: a systematic review. Br J Ophthalmol. 2012;96(9):1162–1167. doi:10.1136/bjophthalmol-2011-301189
  • Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014;121(11):2081–2090. doi:10.1016/j.ophtha.2014.05.013
  • Kapetanakis VV, Chan MP, Foster PJ, Cook DG, Owen CG, Rudnicka AR. Global variations and time trends in the prevalence of primary open angle glaucoma (POAG): a systematic review and meta-analysis. Br J Ophthalmol. 2016;100(1):86–93. doi:10.1136/bjophthalmol-2015-307223
  • Chan EWE, Li X, Tham YC, et al. Glaucoma in Asia: regional prevalence variations and future projections. Br J Ophthalmol. 2016;100(1):78–85. doi:10.1136/bjophthalmol-2014-306102
  • Gupta P, Zhao D, Guallar E, Ko F, Boland MV, Friedman DS. Prevalence of glaucoma in the United States: the 2005–2008 national health and nutrition examination survey. Invest Ophthalmol Vis Sci. 2016;57(6):2905–2913. doi:10.1167/iovs.15-18469
  • Flaxman SR, Bourne RR, Resnikoff S, et al. Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob Health. 2017;5(12):e1221–e1234. doi:10.1016/S2214-109X(17)30393-5
  • Khandekar R, Chauhan D, Yasir ZH, Al-Zobidi M, Judaibi R, Edward DP. The prevalence and determinants of glaucoma among 40 years and older Saudi residents in the Riyadh Governorate (except the Capital)–A community based survey. Saudi J Ophthalmol. 2019;33(4):332–337. doi:10.1016/j.sjopt.2019.02.006
  • Zhang N, Wang J, Chen B, Li Y, Jiang B. Prevalence of primary angle closure glaucoma in the last 20 years: a meta-analysis and systematic review. Front Med. 2020;7:624179.
  • Myers JS, Fudemberg SJ, Lee D. Evolution of optic nerve photography for glaucoma screening: a review. Clin Experiment Ophthalmol. 2018;46(2):169–176. doi:10.1111/ceo.13138
  • Minaee S, Boykov YY, Porikli F, Plaza AJ, Kehtarnavaz N, Terzopoulos D. Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell. 2021;1. doi:10.1109/TPAMI.2021.3059968
  • Ghosh S, Das N, Das I, Maulik U. Understanding deep learning techniques for image segmentation. ACM Comput Surv. 2019;52(4):1–35. doi:10.1145/3329784
  • Chen C, Qin C, Qiu H, et al. Deep learning for cardiac image segmentation: a review. Front Cardiovasc Med. 2020;7:25. doi:10.3389/fcvm.2020.00025
  • Akkus A, Galimzianova Z, Hoogi A, Rubin DL, Erickson BJ. Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging. 2017;30(4):449–459. doi:10.1007/s10278-017-9983-4
  • Işın A, Direkoğlu C, Şah M. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput Sci. 2016;102:317–324. doi:10.1016/j.procs.2016.09.407
  • Rehman A, Khan FG. A deep learning based review on abdominal images. In: Multimedia Tools and Applications; 2020:1–32.
  • Krithiga R, Geetha P. Breast cancer detection, segmentation and classification on histopathology images analysis: a systematic review. Arch Comput Methods Eng. 2021;28(4):2607–2619. doi:10.1007/s11831-020-09470-w
  • Li Z, Zhang J, Tan T, et al. Deep learning methods for lung cancer segmentation in whole-slide histopathology images-the acdc@ lunghp challenge 2019. IEEE J Biomed Health Inform. 2020;25(2):429–440.
  • Yamanakkanavar N, Choi JY, Lee B. MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer’s disease: a survey. Sensors. 2020;20(11):3243. doi:10.3390/s20113243
  • Liu J, Pan Y, Li M, et al. Applications of deep learning to MRI images: a survey. Big Data Min Anal. 2018;1(1):1–18.
  • Domingues I, Pereira G, Martins P, Duarte H, Santos J, Abreu PH. Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET. Artif Intell Rev. 2020;53(6):4093–4160. doi:10.1007/s10462-019-09788-3
  • Akkus Z, Cai J, Boonrod A, et al. A survey of deep-learning applications in ultrasound: artificial intelligence–powered ultrasound for improving clinical workflow. J Am Coll Radiol. 2019;16(9):1318–1328. doi:10.1016/j.jacr.2019.06.004
  • Balyen L, Peto T. Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology. Asia Pac J Ophthalmol. 2019;8(3):264–272. doi:10.22608/APO.2018479
  • Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167–175. doi:10.1136/bjophthalmol-2018-313173
  • Thompson AC, Jammal AA, Medeiros FA. A review of deep learning for screening, diagnosis, and detection of glaucoma progression. Transl Vis Sci Technol. 2020;9(2):42. doi:10.1167/tvst.9.2.42
  • Ting DS, Peng L, Varadarajan AV, et al. Deep learning in ophthalmology: the technical and clinical considerations. Prog Retin Eye Res. 2019;72:100759. doi:10.1016/j.preteyeres.2019.04.003
  • Sengupta S, Singh A, Leopold HA, Gulati T, Lakshminarayanan V. Ophthalmic diagnosis using deep learning with fundus images–A critical review. Artif Intell Med. 2020;102:101758. doi:10.1016/j.artmed.2019.101758
  • Thakur N, Juneja M. Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomed Signal Process Control. 2018;42:162–189. doi:10.1016/j.bspc.2018.01.014
  • Hagiwara Y, Koh JEW, Tan JH, et al. Computer-aided diagnosis of glaucoma using fundus images: a review. Comput Methods Programs Biomed. 2018;165:1–12. doi:10.1016/j.cmpb.2018.07.012
  • Barros DM, Moura JC, Freire CR, Taleb AC, Valentim RA, Morais PS. Machine learning applied to retinal image processing for glaucoma detection: review and perspective. Biomed Eng Online. 2020;19(1):1–21. doi:10.1186/s12938-020-00767-2
  • Mursch-Edlmayr AS, Ng WS, Diniz-Filho A, et al. Artificial intelligence algorithms to diagnose glaucoma and detect glaucoma progression: translation to clinical practice. Transl Vis Sci Technol. 2020;9(2):55. doi:10.1167/tvst.9.2.55
  • Almazroa A, Burman R, Raahemifar K, Lakshminarayanan V. Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. J Ophthalmol. 2015;2015. doi:10.1155/2015/180972
  • Almazroa A, Sun W, Alodhayb S, Raahemifar K, Lakshminarayanan V. Optic disc segmentation for glaucoma screening system using fundus images. Clin Ophthalmol. 2017;11:2017–2029. doi:10.2147/OPTH.S140061
  • Almazroa A, Alodhayb S, Raahemifar K, Lakshminarayanan V. An automatic image processing system for glaucoma screening. Int J Biomed Imaging. 2017;2017. doi:10.1155/2017/4826385
  • Almazroa A, Alodhayb S, Raahemifar K, Lakshminarayanan V. Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction. Clin Ophthalmol. 2017;11:841. doi:10.2147/OPTH.S117157
  • Gopalakrishnan A, Almazroa A, Raahemifar K, Lakshminarayanan V. Optic disc segmentation using circular Hough transform and curve fitting. In: 2015 2nd International Conference on Opto-Electronics and Applied Optics (IEM OPTRONIX); IEEE; October 2015:1–4.
  • Biran A, Bidari PS, Almazroa A, Lakshminarayanan V, Raahemifar K. Blood vessels extraction from retinal images using combined 2D Gabor wavelet transform with local entropy thresholding and alternative sequential filter. In: 2016 IEEE Canadian conference on electrical and computer engineering (CCECE); IEEE; May, 2016:1–5.
  • Almazroa A, Alodhayb S, Burman R, Sun W, Raahemifar K, Lakshminarayanan V. Optic cup segmentation based on extracting blood vessel kinks and cup thresholding using Type-II fuzzy approach. In: 2015 2nd International Conference on Opto-Electronics and Applied Optics (IEM OPTRONIX); IEEE; October, 2015:1–3.
  • Burman R, Almazroa A, Raahemifar K, Lakshminarayanan V. Automated detection of optic disc in fundus images. In: Advances in Optical Science and Engineering. New Delhi: Springer; 2015:327–334.
  • Almazroa A, Sun W, Alodhayb S, Raahemifar K, Lakshminarayanan V. Optic disc segmentation: level set methods and blood vessels inpainting. In: Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications. Vol. 10138. International Society for Optics and Photonics; March, 2017:1013806.
  • Almazroa AA, Woodward MA, Newman-Casey PA, et al. The appropriateness of digital diabetic retinopathy screening images for a computer-aided glaucoma screening system. Clin Ophthalmol. 2020;14:3881. doi:10.2147/OPTH.S273659
  • Eswari MS, Karkuzhali S. Survey on segmentation and classification methods for diagnosis of glaucoma. In: 2020 International Conference on Computer Communication and Informatics (ICCCI); IEEE; January, 2020:1–6.
  • Fumero F, Alayón S, Sanchez JL, Sigut J, Gonzalez-Hernandez M. RIM-ONE: an open retinal image database for optic nerve evaluation. In: 2011 24th international symposium on computer-based medical systems (CBMS); IEEE; June, 2011:1–6.
  • Orlando JI, Fu H, Breda JB, et al. Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Med Image Anal. 2020;59:101570. doi:10.1016/j.media.2019.101570
  • Sivaswamy J, Krishnadas SR, Joshi GD, Jain M, Tabish AUS. Drishti-gs: retinal image dataset for optic nerve head (onh) segmentation. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI); IEEE; April, 2014:53–56.
  • Carmona EJ, Rincón M, García-Feijoó J, Martínez-de-la-casa JM. Identification of the optic nerve head with genetic algorithms. Artif Intell Med. 2008;43(3):243–259. doi:10.1016/j.artmed.2008.04.005
  • Cheng J, Zhang Z, Tao D, et al. Similarity regularized sparse group lasso for cup to disc ratio computation. Biomed Opt Express. 2017;8(8):3763–3777. doi:10.1364/BOE.8.003763
  • Zhang Z, Yin FS, Liu J, et al. Origa-light: an online retinal fundus image database for glaucoma analysis and research. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology; IEEE; August, 2010:3065–3068.
  • Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the Riga dataset. In: Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. Vol. 10579. International Society for Optics and Photonics; March, 2018:105790B.
  • Li L, Xu M, Liu H, et al. A large-scale database and a CNN model for attention-based glaucoma detection. IEEE Trans Med Imaging. 2019;39(2):413–424. doi:10.1109/TMI.2019.2927226
  • Diaz-Pinto A, Morales S, Naranjo V, et al. CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomed Eng Online. 2019;18(1):1–19. doi:10.1186/s12938-019-0649-y
  • Menditto A, Patriarca M, Magnusson B. Understanding the meaning of accuracy, trueness and precision. Accreditation Qual Assur. 2007;12(1):45–47. doi:10.1007/s00769-006-0191-z
  • Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Technol. 2011;2(1):37-63. doi:10.48550/arXiv.2010.16061
  • Dice LR. Measures of the Amount of Ecologic Association Between Species. Ecology. 1945;26:297-302. doi:10.2307/1932409.
  • Jaccard P. The distribution of the flora in the alpine zone. New Phytologist. 1912;11:37-50. doi:10.1111/j.1469-8137.1912.tb05611.x
  • Pont-Tuset J, Marques F. Supervised evaluation of image segmentation and object proposal techniques. IEEE Trans Pattern Anal Mach Intell. 2015;38(7):1465–1478. doi:10.1109/TPAMI.2015.2481406
  • Margolin R, Zelnik-Manor L, Tal A. How to evaluate foreground maps? Proceedings of the IEEE conference on computer vision and pattern recognition; 2014:248–255.
  • Jiang Y, Tan N, Peng T. Optic disc and cup segmentation based on deep convolutional generative adversarial networks. IEEE Access. 2019;7:64483–64493. doi:10.1109/ACCESS.2019.2917508
  • Tian Z, Zheng Y, Li X, Du S, Xu X. Graph convolutional network based optic disc and cup segmentation on fundus images. Biomed Opt Express. 2020;11(6):3043–3057. doi:10.1364/BOE.390056
  • Liu B, Pan D, Song H. Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network. BMC Med Imaging. 2021;21(1):1–12. doi:10.1186/s12880-020-00528-6
  • Kuruvilla J, Sukumaran D, Sankar A, Joy SP. A review on image processing and image segmentation. In: 2016 international conference on data mining and advanced computing (SAPIENCE); IEEE; March, 2016:198–203.
  • Treml M, Arjona-Medina J, Unterthiner T, et al. Speeding up semantic segmentation for autonomous driving; 2016.
  • Tabassum M, Khan TM, Arsalan M, et al. CDED-Net: joint segmentation of optic disc and optic cup for glaucoma screening. IEEE Access. 2020;8:102733–102747. doi:10.1109/ACCESS.2020.2998635
  • Wang L, Liu H, Lu Y, Chen H, Zhang J, Pu J. A coarse-to-fine deep learning framework for optic disc segmentation in fundus images. Biomed Signal Process Control. 2019;51:82–89. doi:10.1016/j.bspc.2019.01.022
  • Kälviäinen RVJPH, Uusitalo H. DIARETDB1 diabetic retinopathy database and evaluation protocol. In: Medical Image Understanding and Analysis. Vol. 2007. Citeseer; 2007:61.
  • Chakravarty A, Sivswamy J. A deep learning based joint segmentation and classification framework for glaucoma assessment in retinal color fundus images; 2018. Available from: https://arxiv.org/abs/1808.01355. Accessed February 26, 2022.
  • Gu Z, Cheng J, Fu H, et al. Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging. 2019;38(10):2281–2292. doi:10.1109/TMI.2019.2903562
  • Al-Bander B, Williams BM, Al-Nuaimy W, Al-Taee MA, Pratt H, Zheng Y. Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis. Symmetry. 2018;10(4):87. doi:10.3390/sym10040087
  • Fu H, Cheng J, Xu Y, et al. Disc-aware ensemble network for glaucoma screening from fundus image. IEEE Trans Med Imaging. 2018;37(11):2493–2501. doi:10.1109/TMI.2018.2837012
  • Zhang Z, Fu H, Dai H, Shen J, Pang Y, Shao L. Et-net: a generic edge-attention guidance network for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention; October, 2019:442–450; Springer, Cham.
  • Bisneto TRV, de Carvalho Filho AO, Magalhães DMV. Generative adversarial network and texture features applied to automatic glaucoma detection. Appl Soft Comput. 2020;90:106165. doi:10.1016/j.asoc.2020.106165
  • Zilly J, Buhmann JM, Mahapatra D. Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput Med Imaging Graph. 2017;55:28–41. doi:10.1016/j.compmedimag.2016.07.012
  • Ding F, Yang G, Liu J, et al. Hierarchical attention networks for medical image segmentation; 2019. Available from: https://arxiv.org/abs/1911.08777. Accessed February 26, 2022.
  • Fu H, Cheng J, Xu Y, Wong DWK, Liu J, Cao X. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans Med Imaging. 2018;37(7):1597–1605. doi:10.1109/TMI.2018.2791488
  • Liu S, Hong J, Lu X, et al. Joint optic disc and cup segmentation using semi-supervised conditional GANs. Comput Biol Med. 2019;115:103485. doi:10.1016/j.compbiomed.2019.103485
  • Kim M, Han JC, Hyun SH, et al. Medinoid: computer-aided diagnosis and localization of glaucoma using deep learning. Appl Sci. 2019;9(15):3064. doi:10.3390/app9153064
  • Sevastopolsky A. Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognit Image Anal. 2017;27(3):618–624. doi:10.1134/S1054661817030269
  • Sun X, Xu Y, Zhao W, You T, Liu J. Optic disc segmentation from retinal fundus images via deep object detection networks. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC); IEEE; July, 2018:5954–5957.
  • Wang S, Yu L, Yang X, Fu CW, Heng PA. Patch-based output space adversarial learning for joint optic disc and cup segmentation. IEEE Trans Med Imaging. 2019;38(11):2485–2495. doi:10.1109/TMI.2019.2899910
  • Singh VK, Rashwan HA, Akram F, et al. Retinal optic disc segmentation using conditional generative adversarial network. In: CCIA; October, 2018:373–380.
  • Yu S, Xiao D, Frost S, Kanagasingam Y. Robust optic disc and cup segmentation with deep learning for glaucoma detection. Comput Med Imaging Graph. 2019;74:61–71. doi:10.1016/j.compmedimag.2019.02.005
  • Joshua AO, Nelwamondo FV, Mabuza-Hocquet G. Segmentation of optic cup and disc for diagnosis of glaucoma on retinal fundus images. In: 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA); IEEE; January, 2019:183–187.
  • Sevastopolsky A, Drapak S, Kiselev K, Snyder BM, Keenan JD, Georgievskaya A. Stack-u-net: refinement network for improved optic disc and cup image segmentation. In: Medical Imaging 2019: Image Processing. Vol. 10949. International Society for Optics and Photonics; March, 2019:1094928.
  • Sedai S, Roy P, Mahapatra D, Garnavi R. Segmentation of optic disc and optic cup in retinal fundus images using coupled shape regression. Proceedings of the Ophthalmic Medical Image Analysis International Workshop. Athens, Greece; 2016.
  • Son J, Park SJ, Jung KH. 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. doi:10.1007/s10278-018-0126-3
  • Bajwa MN, Malik MI, Siddiqui SA, et al. Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Med Inform Decis Mak. 2019;19(1):1–16. doi:10.1186/s12911-018-0723-6
  • Budai A, Odstrcilik J, Kolar R, et al. A public database for the evaluation of fundus image segmentation algorithms. Invest Ophthalmol Vis Sci. 2011;52(14):1345.
  • Zhao R, Liao W, Zou B, Chen Z, Li S. Weakly-supervised simultaneous evidence identification and segmentation for automated glaucoma diagnosis. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33, No. 01; July, 2019:809–816.
  • Sreng S, Maneerat N, Hamamoto K, Win KY. Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images. Appl Sci. 2020;10(14):4916. doi:10.3390/app10144916
  • Tan JH, Acharya UR, Bhandary SV, Chua KC, Sivaprasad S. Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J Comput Sci. 2017;20:70–79. doi:10.1016/j.jocs.2017.02.006
  • Zilly JG, Buhmann JM, Mahapatra D. Boosting convolutional filters with entropy sampling for optic cup and disc image segmentation from fundus images. In: International Workshop on Machine Learning in Medical Imaging. Cham: Springer; October, 2015:136–143.
  • Yao Z, Zhang Z, Xu LQ. Convolutional neural network for retinal blood vessel segmentation. In: 2016 9th international symposium on Computational intelligence and design (ISCID). Vol. 1. IEEE; December, 2016:406–409.
  • Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV); 2018:801–818.
  • He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell. 2015;37(9):1904–1916. doi:10.1109/TPAMI.2015.2389824
  • Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst. 2015;28:91–99.
  • Lu Z, Chen D, Xue D, Zhang S. Weakly supervised semantic segmentation for optic disc of fundus image. J Electron Imaging. 2019;28(3):033012.
  • Shankaranarayana S, Ram SM, Mitra K, Sivaprakasam M. Fully convolutional networks for monocular retinal depth estimation and optic disc-cup segmentation. IEEE J Biomed Health Inform. 2019;23(4):1417–1426. doi:10.1109/JBHI.2019.2899403
  • Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention; October, 2015:234–241; Springer, Cham.
  • Maninis KK, Pont-Tuset J, Arbeláez P, Van Gool L. Deep retinal image understanding. In: International conference on medical image computing and computer-assisted intervention; October, 2016:140–148; Springer, Cham.
  • Kingma DP, Welling M. Auto-encoding variational Bayes; 2013. Available from: https://arxiv.org/abs/1312.6114. Accessed February 26, 2022.
  • Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. Commun ACM. 2020;63(11):139–144. doi:10.1145/3422622
  • Luc P, Couprie C, Chintala S, Verbeek J. Semantic segmentation using adversarial networks; 2016. Available from: https://arxiv.org/abs/1611.08408. Accessed February 26, 2022.
  • Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing. 2018;321:321–331. doi:10.1016/j.neucom.2018.09.013
  • Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA. Generative adversarial networks: an overview. IEEE Signal Process Mag. 2018;35(1):53–65. doi:10.1109/MSP.2017.2765202
  • Isola P, Zhu JY, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017:1125–1134.
  • Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition; 2014. Available from: https://arxiv.org/abs/1409.1556. Accessed February 26, 2022.
  • Wang S, Yu L, Heng PA. Optic disc and cup segmentation with output space domain adaptation. Refug Rep. 2019. Available from: http://rumc-gcorg-p-public.s3.amazonaws.com/f/challenge/229/431a27fd-58e3-4719-88d7-d59929a4e8b1/REFUGE-CUHKMED.pdf. Accessed February 26, 2022.
  • Almazroa A, Alodhayb S, Osman E, et al. Agreement among ophthalmologists in marking the optic disc and optic cup in fundus images. Int Ophthalmol. 2017;37(3):701–717. doi:10.1007/s10792-016-0329-x
  • Wang M, Deng W. Deep visual domain adaptation: a survey. Neurocomputing. 2018;312:135–153. doi:10.1016/j.neucom.2018.05.083
  • Krishna Adithya V, Williams BM, Czanner S, et al. EffUnet-SpaGen: an efficient and spatial generative approach to glaucoma detection. J Imaging. 2021;7(6):92. doi:10.3390/jimaging7060092
  • Jin B, Liu P, Wang P, Shi L, Zhao J. Optic disc segmentation using attention-based U-Net and the improved cross-entropy convolutional neural network. Entropy. 2020;22(8):844. doi:10.3390/e22080844
  • Yousefi S, Pasquale LR, Boland MV. Artificial intelligence and glaucoma: illuminating the black box. Ophthalmol Glaucoma. 2020;3(5):311–313. doi:10.1016/j.ogla.2020.04.008
  • Nguyen HV, Tan GS, Tapp RJ, et al. Cost-effectiveness of a national telemedicine diabetic retinopathy screening program in Singapore. Ophthalmology. 2016;123(12):2571–2580. doi:10.1016/j.ophtha.2016.08.021
  • Grzybowski A, Brona P, Lim G, et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye. 2020;34(3):451–460. doi:10.1038/s41433-019-0566-0