Retinal Image Synthesis for CAD Development

  • Pujitha Appan K.Email author
  • Jayanthi Sivaswamy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)


Automatic disease detection and classification have been attracting much interest. High performance is critical in adoption of such systems, which generally rely on training with a wide variety of annotated data. Availability of such varied annotated data in medical imaging is very scarce. Synthetic data generation is a promising solution to address this problem. We propose a novel method, based on generative adversarial networks (GAN), to generate images with lesions such that the overall severity level can be controlled. We demonstrate the reliability of the generated synthetic images independently as well as by training a computer aided diagnosis (CAD) system with the generated data. We showcase this approach for heamorrhage detection in retinal images with 4 levels of severity. Quantitative assessment results show that the generated synthetic images are very close to the real data. Haemorrhage detection was found to improve with inclusion of synthetic data in the training set with improvements in sensitivity ranging from 20% to 27% over training with just expert marked data.


Synthetic images Generative adversarial networks Deep neural net 


  1. 1.
    Collins, D.L., et al.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imaging 17(3), 463–468 (1998)CrossRefGoogle Scholar
  2. 2.
    Costa, P., et al.: End-to-end adversarial retinal image synthesis. IEEE Trans. Med. Imaging 37(3), 781–791 (2017)CrossRefGoogle Scholar
  3. 3.
    Decenciere, E., et al.: Feedback on a publicly distributed database: the messidor database. Image Anal. Stereol. 33, 231–234 (2014)CrossRefGoogle Scholar
  4. 4.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27, pp. 2672–2680 (2014)Google Scholar
  5. 5.
    van Grinsven, M.J.J.P., et al.: Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans. Med. Imaging 35, 1273–1284 (2016)CrossRefGoogle Scholar
  6. 6.
    Guibas, J.T., et al.: Synthetic Medical Images from Dual Generative Adversarial Networks. ArXiv e-prints, September 2017Google Scholar
  7. 7.
    Joshi, G.D., et al.: Colour retinal image enhancement based on domain knowledge. In: Computer Vision, Graphics Image Processing, ICVGIP, pp. 591–598, December 2008Google Scholar
  8. 8.
    Kalesnykiene, V., et al.: DIARETDB1 diabetic retinopathy database and evaluation protocol, June 2007Google Scholar
  9. 9.
    Kohler, T., et al.: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In: International Symposium on Computer-Based Medical Systems, CBMS 2013, pp. 95–100 (2013)Google Scholar
  10. 10.
    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). Scholar
  11. 11.
    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). Scholar
  12. 12.
    Nie, D., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q., Shen, D.: Medical image synthesis with context-aware generative adversarial networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 417–425. Springer, Cham (2017). Scholar
  13. 13.
    Prastawa, M., et al.: Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Med. Image Anal. 13, 297–311 (2009)CrossRefGoogle Scholar
  14. 14.
    Prentasic, P., et al.: Diabetic retinopathy image database (DRiDB): a new database for diabetic retinopathy screening programs research. In: Image and Signal Processing and Analysis, ISPA, pp. 704–709 (2013)Google Scholar
  15. 15.
    Rezaei, M., et al.: Conditional adversarial network for semantic segmentation of brain tumor. CoRR abs/1708.05227, August 2017Google Scholar
  16. 16.
    Ronneberger, O., et al.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention, MICCAI, pp. 234–241 (2015)Google Scholar
  17. 17.
    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). Scholar
  18. 18.
    Wang, Z., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  19. 19.
    Wilkinson, C.P., et al.: Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110(9), 1677–1682 (2003)CrossRefGoogle Scholar
  20. 20.
    Yang, Y., et al.: Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. CoRR abs/1705.00771 (2017)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Visual Information TechnologyHyderabadIndia

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