Advertisement

Multiscale Network Followed Network Model for Retinal Vessel Segmentation

  • Yicheng Wu
  • Yong XiaEmail author
  • Yang Song
  • Yanning Zhang
  • Weidong Cai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

The shape of retinal blood vessels plays a critical role in the early diagnosis of diabetic retinopathy. However, it remains challenging to segment accurately the blood vessels, particularly the capillaries, in color retinal images. In this paper, we propose the multiscale network followed network (MS-NFN) model to address this issue. This model consists of an ‘up-pool’ NFN submodel and a ‘pool-up’ NFN submodel, in which max-pooling layers and up-sampling layers can generate multiscale feature maps. In each NFN, the first multiscale network converts an image patch into a probabilistic retinal vessel map, and the following multiscale network further refines the map. The refined probabilistic retinal vessel maps produced by both NFNs are averaged to construct the segmentation result. We evaluated this model on the digital retinal images for vessel extraction (DRIVE) dataset and the child heart and health study Open image in new window dataset. Our results indicate that the NFN structure we designed is able to produce performance gain and the proposed MS-NFN model achieved the state-of-the-art retinal vessel segmentation accuracy on both datasets.

Keywords

Retinal vessel segmentation Network followed network Fully convolutional network 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61771397 and 61471297, in part by the China Postdoctoral Science Foundation under Grant 2017M623245, in part by the Fundamental Research Funds for the Central Universities under Grant 3102018zy031, and in part by the Australian Research Council (ARC) Grants. We also appreciate the efforts devoted to collect and share the DRIVE and Open image in new window datasets for retinal vessel segmentation.

References

  1. 1.
    Saker, S.: Diabetic retinopathy: in vitro and clinical studies of mechanisms and pharmacological treatments. University of Nottingham (2016)Google Scholar
  2. 2.
    Lupascu, C.A., Tegolo, D., Trucco, E.: FABC: retinal vessel segmentation using AdaBoost. IEEE Trans. Inf. Technol. Biomed. 14, 1267–1274 (2010)CrossRefGoogle Scholar
  3. 3.
    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, 501–509 (2004)CrossRefGoogle Scholar
  4. 4.
    Lam, B.S.Y., Yan, H.: A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. IEEE Trans. Med. Imaging 27, 237–246 (2008)CrossRefGoogle Scholar
  5. 5.
    Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35, 2369–2380 (2016)CrossRefGoogle Scholar
  6. 6.
    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 Trans. Med. Imaging 35, 109–118 (2016)CrossRefGoogle Scholar
  7. 7.
    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_28CrossRefGoogle Scholar
  8. 8.
    Fraz, M.M.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59, 2538–2548 (2012)CrossRefGoogle Scholar
  9. 9.
    Owen, C.G.: Measuring retinal vessel tortuosity in 10-year-old children: validation of the Computer-assisted image analysis of the retina (CAIAR) program. Invest. Ophthalmol. Vis. Sci. 50, 2004–2010 (2009)CrossRefGoogle Scholar
  10. 10.
    Soares, J.V.B., Leandro, J.J.G., 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, 1214–1222 (2006)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Mapayi, T., Viriri, S., Tapamo, J.R.: Adaptive thresholding technique for retinal vessel segmentation based on GLCM-energy information. Comput. Math. Methods Med. 2015, 597475 (2015)Google Scholar
  13. 13.
    Azzopardi, G., Strisciuglio, N., Vento, M., Petkov, N.: Trainable COSFIRE filters for vessel delineation with application to retinal images. Med. Image Anal. 19, 46 (2015)CrossRefGoogle Scholar
  14. 14.
    Roychowdhury, S., Koozekanani, D., Parhi, K.: Blood vessel segmentation of fundus images by major vessel extraction and sub-image classification. IEEE J. Biomed. Health Inform. 19, 1118–1128 (2015)Google Scholar
  15. 15.
    Orlando, J., Prokofyeva, E., Blaschko, M.: A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans. Biomed. Eng. 64, 16–27 (2017)CrossRefGoogle Scholar
  16. 16.
    Dasgupta, A., Singh, S.: A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In: IEEE 14th ISBI, pp. 248–251. IEEE, Melbourne (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yicheng Wu
    • 1
  • Yong Xia
    • 1
    Email author
  • Yang Song
    • 2
  • Yanning Zhang
    • 1
  • Weidong Cai
    • 2
  1. 1.School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China
  2. 2.School of Information TechnologiesUniversity of SydneySydneyAustralia

Personalised recommendations