Skip to main content
SpringerLink
Log in

Generative Adversarial Networks (GANs) for Retinal Fundus Image Synthesis

  • Conference paper
  • First Online:
Computer Vision – ACCV 2018 Workshops (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11367))

Included in the following conference series:

Abstract

The lack of access to large annotated datasets and legal concerns regarding patient privacy are limiting factors for many applications of deep learning in the retinal image analysis domain. Therefore the idea of generating synthetic retinal images, indiscernible from real data, has gained more interest. Generative adversarial networks (GANs) have proven to be a valuable framework for producing synthetic databases of anatomically consistent retinal fundus images. In Ophthalmology, GANs in particular have shown increased interest. We discuss here the potential advantages and limitations that need to be addressed before GANs can be widely adopted for retinal imaging.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alex, V., Mohammed Safwan, K.P., Chennamsetty, S.S., Krishnamurthi, G.: Generative adversarial networks for brain lesion detection. In: Medical Imaging 2017: Image Processing, vol. 10133, p. 101330G. International Society for Optics and Photonics (2017)

    Google Scholar 

  2. Appan, K.P., Sivaswamy, J.: Retinal image synthesis for CAD development. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 613–621. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_70

    Chapter  Google Scholar 

  3. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)

  4. Baur, C., Albarqouni, S., Navab, N.: MelanoGANs: High resolution skin lesion synthesis with GANs. arXiv preprint arXiv:1804.04338 (2018)

  5. Beers, A., et al.: High-resolution medical image synthesis using progressively grown generative adversarial networks. arXiv preprint arXiv:1805.03144 (2018)

  6. Blitzer, J., McDonald, R., Pereira, F.: Domain adaptation with structural correspondence learning. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 120–128. Association for Computational Linguistics (2006)

    Google Scholar 

  7. Burlina, P.M., Joshi, N., Pekala, M., Pacheco, K.D., Freund, D.E., Bressler, N.M.: Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 135(11), 1170–1176 (2017)

    Article  Google Scholar 

  8. Chen, X., Pawlowski, N., Rajchl, M., Glocker, B., Konukoglu, E.: Deep generative models in the real-world: an open challenge from medical imaging. arXiv preprint arXiv:1806.05452 (2018)

  9. Cheung, N., et al.: Prevalence and risk factors for epiretinal membrane: the Singapore epidemiology of eye disease study. Br. J. Ophthalmol. 101(3), 371–376 (2017)

    Google Scholar 

  10. Cheung, N., et al.: Prevalence and associations of retinal emboli with ethnicity, stroke, and renal disease in a multiethnic asian population: the Singapore epidemiology of eye disease study. JAMA Ophthalmol. 135(10), 1023–1028 (2017)

    Article  Google Scholar 

  11. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)

  12. Dysmorphology Subcommittee of the Clinical Practice Committee: informed consent for medical photographs. Genet. Med. 2(6), 353 (2000)

    Google Scholar 

  13. Costa, P., et al.: Towards adversarial retinal image synthesis. arXiv preprint arXiv:1701.08974 (2017)

  14. Costa, P., et al.: End-to-end adversarial retinal image synthesis. IEEE Trans. Med. Imaging 37(3), 781–791 (2018)

    Article  Google Scholar 

  15. Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Sig. Process. Mag. 35(1), 53–65 (2018)

    Article  Google Scholar 

  16. Ding, J., Wong, T.Y.: Current epidemiology of diabetic retinopathy and diabetic macular edema. Curr. Diab. Rep. 12(4), 346–354 (2012)

    Article  Google Scholar 

  17. Fiorini, S., Ballerini, L., Trucco, E., Ruggeri, A.: Automatic generation of synthetic retinal fundus images. In: Eurographics Italian Chapter Conference, pp. 41–44 (2014)

    Google Scholar 

  18. Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)

    Article  Google Scholar 

  19. Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)

    Google Scholar 

  20. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  21. Grassmann, F., et al.: A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology 125(9), 1410–1420 (2018)

    Article  Google Scholar 

  22. Guibas, J.T., Virdi, T.S., Li, P.S.: Synthetic medical images from dual generative adversarial networks. arXiv preprint arXiv:1709.01872 (2017)

  23. Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)

    Article  Google Scholar 

  24. Iqbal, T., Ali, H.: Generative adversarial network for medical images (MI-GAN). J. Med. Syst. 42(11), 231 (2018)

    Article  Google Scholar 

  25. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint (2017)

    Google Scholar 

  26. Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)

  27. Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)

  28. Koh, V.K., et al.: Retinal vein occlusion in a multi-ethnic Asian population: the Singapore epidemiology of eye disease study. Ophthalmic Epidemiol. 23(1), 6–13 (2016)

    Article  Google Scholar 

  29. Köhler, T., Budai, A., Kraus, M.F., Odstrčilik, J., Michelson, G., Hornegger, J.: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In: 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS), pp. 95–100. IEEE (2013)

    Google Scholar 

  30. Lahiri, A., Ayush, K., Biswas, P.K., Mitra, P.: Generative adversarial learning for reducing manual annotation in semantic segmentation on large scale miscroscopy images: automated vessel segmentation in retinal fundus image as test case. In: Conference on Computer Vision and Pattern Recognition Workshops, pp. 42–48 (2017)

    Google Scholar 

  31. Lee, C.S., Tyring, A.J., Deruyter, N.P., Wu, Y., Rokem, A., Lee, A.Y.: Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed. Opt. Express 8(7), 3440–3448 (2017)

    Article  Google Scholar 

  32. Li, Z., He, Y., Keel, S., Meng, W., Chang, R.T., He, M.: Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 125(8), 1199–1206 (2018)

    Article  Google Scholar 

  33. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  34. Mahapatra, D.: Retinal vasculature segmentation using local saliency maps and generative adversarial networks for image super resolution. arXiv preprint arXiv:1710.04783 (2017)

  35. Maninis, K.-K., Pont-Tuset, J., Arbeláez, P., Van Gool, L.: Deep retinal image understanding. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 140–148. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_17

    Chapter  Google Scholar 

  36. Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Smolley, S.P.: Least squares generative adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2813–2821. IEEE (2017)

    Google Scholar 

  37. Menti, E., Bonaldi, L., Ballerini, L., Ruggeri, A., Trucco, E.: Automatic generation of synthetic retinal fundus images: vascular network. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2016. LNCS, vol. 9968, pp. 167–176. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46630-9_17

    Chapter  Google Scholar 

  38. Mirza, M., Osindero, S.: Conditional generative adversarial networks. https://arxiv.org/abs/1709.02023 (2014)

  39. Niemeijer, M., Abramoff, M.D., van Ginneken, B.: Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med. Image Anal. 10(6), 888–898 (2006)

    Article  Google Scholar 

  40. Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. arXiv preprint arXiv:1610.09585 (2016)

  41. Pujitha, A.K., Sivaswamy, J.: Solution to overcome the sparsity issue of annotated data in medical domain. CAAI Trans. Intell. Technol. 3(3), 153–160 (2018)

    Article  Google Scholar 

  42. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)

  43. Rezaei, M., Yang, H., Meinel, C.: Whole heart and great vessel segmentation with context-aware of generative adversarial networks. Bildverarbeitung für die Medizin 2018. I, pp. 353–358. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-56537-7_89

    Chapter  Google Scholar 

  44. Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082 (2014)

  45. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  46. Salehinejad, H., Valaee, S., Dowdell, T., Colak, E., Barfett, J.: Generalization of deep neural networks for chest pathology classification in x-rays using generative adversarial networks. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 990–994. IEEE (2018)

    Google Scholar 

  47. Shankaranarayana, S.M., Ram, K., Mitra, K., Sivaprakasam, M.: Joint optic disc and cup segmentation using fully convolutional and adversarial networks. In: Cardoso, M.J., et al. (eds.) FIFI/OMIA -2017. LNCS, vol. 10554, pp. 168–176. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67561-9_19

    Chapter  Google Scholar 

  48. Shitrit, O., Raviv, T.R.: Accelerated magnetic resonance imaging by adversarial neural network. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 30–38. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_4

    Chapter  Google Scholar 

  49. Singh, V.K., et al.: Retinal optic disc segmentation using conditional generative adversarial network. arXiv preprint arXiv:1806.03905 (2018)

  50. Son, J., Park, S.J., Jung, K.H.: Retinal vessel segmentation in fundoscopic images with generative adversarial networks. arXiv preprint arXiv:1706.09318 (2017)

  51. Tham, Y.C., Li, X., Wong, T.Y., Quigley, H.A., Aung, T., Cheng, C.Y.: Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121(11), 2081–2090 (2014)

    Article  Google Scholar 

  52. Ting, D.S.W., et al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22), 2211–2223 (2017)

    Article  Google Scholar 

  53. Ting, D.S., Liu, Y., Burlina, P., Xu, X., Bressler, N.M., Wong, T.Y.: AI for medical imaging goes deep. Nat. Med. 24(5), 539 (2018)

    Article  Google Scholar 

  54. Tomczak, J.M., Welling, M.: Improving variational auto-encoders using householder flow. arXiv preprint arXiv:1611.09630 (2016)

  55. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  56. Watt, G.: Using patient records for medical research. Br. J. Gen. Pract. 56(529), 630–631 (2006)

    Google Scholar 

  57. Wolterink, J.M., Leiner, T., Viergever, M.A., Išgum, I.: Generative adversarial networks for noise reduction in low-dose ct. IEEE Trans. Med. Imaging 36(12), 2536–2545 (2017)

    Article  Google Scholar 

  58. Wong, W.L., et al.: Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob. Health 2(2), e106–e116 (2014)

    Article  Google Scholar 

  59. Yau, J.W., et al.: Global prevalence and major risk factors of diabetic retinopathy. Diab. care 35(3), 556–564 (2012)

    Article  Google Scholar 

  60. Zhao, H., Li, H., Maurer-Stroh, S., Cheng, L.: Synthesizing retinal and neuronal images with generative adversarial nets. Med. Image Anal. 49, 14–26 (2018)

    Article  Google Scholar 

  61. Zhao, H., Li, H., Maurer-Stroh, S., Guo, Y., Deng, Q., Cheng, L.: Supervised segmentation of un-annotated retinal fundus images by synthesis. IEEE Trans. Med. Imaging 38(1), 46–56 (2018)

    Article  Google Scholar 

  62. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Singapore Eye Research Institute, Singapore, Singapore

    Valentina Bellemo, Tien Yin Wong & Daniel Shu Wei Ting

  2. Johns Hopkins University, Baltimore, USA

    Philippe Burlina

  3. Institute of High Performance, A*Star, Singapore, Singapore

    Liu Yong

  4. Singapore National Eye Centre, Singapore, Singapore

    Tien Yin Wong & Daniel Shu Wei Ting

  5. Duke-NUS Medical School, Singapore, Singapore

    Tien Yin Wong & Daniel Shu Wei Ting

Authors
  1. Valentina Bellemo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philippe Burlina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liu Yong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tien Yin Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniel Shu Wei Ting
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valentina Bellemo .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Adelaide, Adelaide, Australia

    Gustavo Carneiro

  2. Data61, Commonwealth Scientific and Industrial Research Organization, Canberra, Australia

    Shaodi You

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bellemo, V., Burlina, P., Yong, L., Wong, T.Y., Ting, D.S.W. (2019). Generative Adversarial Networks (GANs) for Retinal Fundus Image Synthesis. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-21074-8_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21073-1

  • Online ISBN: 978-3-030-21074-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation