Deep Classification and Segmentation Model for Vessel Extraction in Retinal Images

  • Yicheng Wu
  • Yong XiaEmail author
  • Yanning Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)


The shape of retinal blood vessels is critical in the early diagnosis of diabetes and diabetic retinopathy. Segmentation of retinal vessels, particularly the capillaries, remains a significant challenge. To address this challenge, in this paper, we adopt the “divide-and-conque” strategy, and thus propose a deep neural network-based classification and segmentation (CAS) model to extract blood vessels in color retinal images. We first use the network in network (NIN) to divide the retinal patches extracted from preprocessed fundus retinal images into wide-vessel, middle-vessel and capillary patches. Then we train three U-Nets to segment three classes of vessels, respectively. Finally, this algorithm has been evaluated on the digital retinal images for vessel extraction (DRIVE) database against seven existing algorithms and achieved the highest AUC of 97.93% and top three accuracy, sensitivity and specificity. Our comparison results indicate that the proposed algorithm is able to segment blood vessels in retinal images with better performance.


Retinal vessels segmentation Deep learning Classification and segmentation 



This work was supported in part by the National Natural Science Foundation of China under Grants 61471297 and 61771397, and in part by the China Postdoctoral Science Foundation under Grant 2017M623245, and in part by the Fundamental Research Funds for the Central Universities under Grant 3102018zy031. We also appreciate the efforts devoted to collect and share the DRIVE database for comparing algorithms of vessels segmentation in color images of the retina.


  1. 1.
    Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE TMI 23(4), 501–509 (2004)Google Scholar
  2. 2.
    Lupascu, C.A., Tegolo, D., Trucco, E.: FABC: retinal vessel segmentation using AdaBoost. IEEE TITB 14(5), 1267–1274 (2010)Google Scholar
  3. 3.
    Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural network. IEEE TMI 35(11), 2369–2380 (2016)Google Scholar
  4. 4.
    Li, Q., Feng, B., Xie, L.P., Liang, P., Zhang, H., Wang, T.: A cross-modality learning approach for vessel segmentation in retinal images. IEEE TMI 35(1), 109–118 (2016)Google Scholar
  5. 5.
    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
  6. 6.
    Fu, H., Xu, Y., Wong, D.W.K., Liu, J.: Retinal vessel segmentation via deep learning net-work and fully-connected conditional random fields. In: ISBI, pp. 698–701. IEEE, Prague (2016)Google Scholar
  7. 7.
    Setiawan, A.W., Mengko, T.R., Santoso, O.S., Suksmono, A.B.: Color retinal image enhancement using CLAHE. In: ICISS, pp. 1–3. IEEE, Jakarta (2013)Google Scholar
  8. 8.
    Lin, M., Chen, Q., Yan, S.: Network in network. In: ICLR. IEEE, Banff (2014)Google Scholar
  9. 9.
    Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE TBE 59(9), 2538–2548 (2012)Google Scholar
  10. 10.
    Lahiri, A., Roy, A.G., Sheet, D., Biswas, P.K.: Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography. In: EMBC, pp. 1340–1343. IEEE, Orlando (2016)Google Scholar
  11. 11.
    Fu, H., Xu, Y., Lin, S., Kee Wong, D.W., Liu, J.: DeepVessel: retinal vessel segmentation via deep learning and conditional random field. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 132–139. Springer, Cham (2016). Scholar
  12. 12.
    Dasgupta, A., Singh, S.: A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In: ISBI, pp. 248–251. IEEE, Melbourne (2017)Google Scholar
  13. 13.
    Orlando, J., Prokofyeva, E., Blaschko, M.: A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE TBE 64(1), 16–27 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anChina

Personalised recommendations