Particle Swarm Optimization for Great Enhancement in Semi-supervised Retinal Vessel Segmentation with Generative Adversarial Networks

  • Qiang Huo
  • Geyu Tang
  • Feng ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11794)


Retinal vessel segmentation based on deep learning requires a lot of manual labeled data. That’s time-consuming, laborious and professional. In this paper, we propose a data-efficient semi-supervised learning framework, which effectively combines the existing deep learning network with generative adversarial networks (GANs) and self-training ideas. In view of the difficulty of tuning hyper-parameters of semi-supervised learning, we propose a method for hyper-parameters selection based on particle swarm optimization (PSO) algorithm. This work is the first demonstration that combines intelligent optimization with semi-supervised learning for achieving the best performance. Under the collaboration of adversarial learning, self-training and PSO, we obtain the performance of retinal vessel segmentation approximate to or even better than representative supervised learning using only one tenth of the labeled data from DRIVE.


Generative adversarial networks Retinal vessel segmentation Particle swarm optimization Semi-supervised learning 


  1. 1.
    Abramoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)CrossRefGoogle Scholar
  2. 2.
    Krause, M., Alles, R.M., Burgeth, B., Weickert, J.: Fast retinal vessel analysis. J. Real-Time Image Proc. 11(2), 413–422 (2016)CrossRefGoogle Scholar
  3. 3.
    Fraz, M.M., et al.: An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput. Methods Programs Biomed. 108(2), 600–616 (2012)CrossRefGoogle Scholar
  4. 4.
    Wang, Y., Ji, G., Lin, P., Trucco, E.: Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition. Pattern Recogn. 46(8), 2117–2133 (2013)CrossRefGoogle Scholar
  5. 5.
    Mapayi, T., Viriri, S., Tapamo, J.-R.: Adaptive thresholding technique for retinal vessel segmentation based on GLCM-energy information. Comput. Math. Methods Med. 2015, 11 (2015)Google Scholar
  6. 6.
    Becker, C., Rigamonti, R., Lepetit, V., Fua, P.: Supervised feature learning for curvilinear structure segmentation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 526–533. Springer, Heidelberg (2013). Scholar
  7. 7.
    Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016)CrossRefGoogle Scholar
  8. 8.
    Maninis, K.-K., Pont-Tuset, J., Arbeláez, P., Van Gool, L.: Deep retinal image understanding. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 140–148. Springer, Cham (2016). Scholar
  9. 9.
    Souly, N., Spampinato, C., Shah, M.: Semi supervised semantic using generative adversarial network. In: IEEE International Conference Computer Vision (ICCV), pp. 5689–5697 (2017)Google Scholar
  10. 10.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  11. 11.
    Esmin, A., Coelho, R., Matwin, S.: A review on particle swarm optimization algorithm and its variants to clustering high dimensional data. Artif. Intell. Rev. 44(1), 23–45 (2015)CrossRefGoogle Scholar
  12. 12.
    Lorenzo, P.R., et al.: Hyper-parameter selection in deep neural networks using parallel particle swarm optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM (2017)Google Scholar
  13. 13.
    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). Scholar
  14. 14.
    Staal, J., et al.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)CrossRefGoogle Scholar
  15. 15.
    Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395–1403 (2015)Google Scholar
  16. 16.
    Soares, J., Leandro, J.J., Cesar, R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Microelectronics of Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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