A Multi-task Network to Detect Junctions in Retinal Vasculature

  • Fatmatülzehra UsluEmail author
  • Anil Anthony Bharath
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Junctions in the retinal vasculature are key points to be able to extract its topology, but they vary in appearance, depending on vessel density, width and branching/crossing angles. The complexity of junction patterns is usually accompanied by a scarcity of labels, which discourages the usage of very deep networks for their detection. We propose a multi-task network, generating labels for vessel interior, centerline, edges and junction patterns, to provide additional information to facilitate junction detection. After the initial detection of potential junctions in junction-selective probability maps, candidate locations are re-examined in centerline probability maps to verify if they connect at least 3 branches. The experiments on the DRIVE and IOSTAR showed that our method outperformed a recent study in which a popular deep network was trained as a classifier to find junctions. Moreover, the proposed approach is applicable to unseen datasets with the same degree of success, after training it only once.


Restricted Boltzmann machines Deep networks Bifurcation Crossing Fundus images 


  1. 1.
    Abbasi-Sureshjani, S., Smit-Ockeloen, I., Bekkers, E., Dashtbozorg, B., ter Haar Romeny, B.: Automatic detection of vascular bifurcations and crossings in retinal images using orientation scores. In: The IEEE International Symposium on Biomedical Imaging (ISBI), pp. 189–192. IEEE (2016)Google Scholar
  2. 2.
    Abbasi-Sureshjani, S., Smit-Ockeloen, I., Zhang, J., Ter Haar Romeny, B.: Biologically-inspired supervised vasculature segmentation in SLO retinal fundus images. In: Kamel, M., Campilho, A. (eds.) ICIAR 2015. LNCS, vol. 9164, pp. 325–334. Springer, Cham (2015). Scholar
  3. 3.
    Azzopardi, G., Petkov, N.: Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters. Pattern Recogn. Lett. 34(8), 922–933 (2013)CrossRefGoogle Scholar
  4. 4.
    Calvo, D., Ortega, M., Penedo, M.G., Rouco, J.: Automatic detection and characterisation of retinal vessel tree bifurcations and crossovers in eye fundus images. Comput. Methods Programs Biomed. 103(1), 28–38 (2011)CrossRefGoogle Scholar
  5. 5.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). Scholar
  6. 6.
    Hamad, H., Tegolo, D., Valenti, C.: Automatic detection and classification of retinal vascular landmarks. Image Anal. Stereol. 33(3), 189–200 (2014)CrossRefGoogle Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  8. 8.
    Hinton, G.E.: A practical guide to training restricted Boltzmann machines. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 599–619. Springer, Heidelberg (2012). Scholar
  9. 9.
    Li, Q., Feng, B., Xie, L., Liang, P., Zhang, H., Wang, T.: A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Med. Imaging 35(1), 109–118 (2016)CrossRefGoogle Scholar
  10. 10.
    Pratt, H., et al.: Automatic detection and distinction of retinal vessel bifurcations and crossings in colour fundus photography. J. Imaging 4(1), 4 (2017)CrossRefGoogle Scholar
  11. 11.
    Soares, J.V., 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
  12. 12.
    Srinidhi, C.L., Aparna, P., Rajan, J.: Recent advancements in retinal vessel segmentation. J. Med. Syst. 41(4), 70 (2017)CrossRefGoogle Scholar
  13. 13.
    Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)CrossRefGoogle Scholar
  14. 14.
    Zhang, H., Yang, Y., Shen, H.: Line junction detection without prior-delineation of curvilinear structure in biomedical images. IEEE Access 6, 2016–2027 (2018)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.BICV Group, Bioengineering DepartmentImperial College LondonLondonUK

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