A Deep Learning Approach to Detect the Demarcation Line in OCT Images

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1248)


Corneal cross-linking (CXL) is a surgical intervention to treat the progression of an eye disease called keratoconus that may lead to significant loss of visual acuity. Manually detecting the presence and the depth of a stromal demarcation line in optical coherence tomography (OCT) images is a standard procedure used by ophthalmologists to check the success of CXL. In this paper, we propose a deep learning model trained in a semi-weakly supervised fashion to segment the area between the top boundary of the cornea and the demarcation line that is later used by our extraction algorithm to obtain the demarcation line automatically. We report an improvement in performance compared to the fully supervised learning approaches in terms of the dice coefficient.


  1. 1.
    Asaoka, R., Murata, H., Iwase, A., Araie, M.: Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology 123(9), 1974–1980 (2016)CrossRefGoogle Scholar
  2. 2.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)CrossRefGoogle Scholar
  3. 3.
    Bogunović, H., et al.: Machine learning of the progression of intermediate age-related macular degeneration based on oct imaging. Invest. Ophthalmol. Vis. Sci. 58(6), BIO141–BIO150 (2017)Google Scholar
  4. 4.
    Chen, X., Xu, Y., Yan, S., Wong, D.W.K., Wong, T.Y., Liu, J.: Automatic feature learning for glaucoma detection based on deep learning. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 669–677. Springer, Cham (2015). Scholar
  5. 5.
    Dhaini, A.R., Chokr, M., El-Oud, S.M., Fattah, M.A., Awwad, S.: Automated detection and measurement of corneal haze and demarcation line in spectral-domain optical coherence tomography images. IEEE Access 6, 3977–3991 (2018)CrossRefGoogle Scholar
  6. 6.
    Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)CrossRefGoogle Scholar
  7. 7.
    Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama 316(22), 2402–2410 (2016)CrossRefGoogle Scholar
  8. 8.
    Karri, S.P.K., Chakraborty, D., Chatterjee, J.: Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed. Opt. Express 8(2), 579–592 (2017)CrossRefGoogle Scholar
  9. 9.
    Muhammad, H., et al.: Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects. J. Glaucoma 26(12), 1086 (2017)CrossRefGoogle Scholar
  10. 10.
    Peng, Y., et al.: DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology 126(4), 565–575 (2019)CrossRefGoogle Scholar
  11. 11.
    Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P., Zheng, Y.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200–205 (2016)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Suzuki, S., et al.: Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30(1), 32–46 (1985)CrossRefGoogle Scholar
  14. 14.
    Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  15. 15.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)Google Scholar
  16. 16.
    Treder, M., Lauermann, J.L., Eter, N.: Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning. Graefe’s Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2017). Scholar
  17. 17.
    Wang, Z., Yin, Y., Shi, J., Fang, W., Li, H., Wang, X.: Zoom-in-Net: deep mining lesions for diabetic retinopathy detection. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 267–275. Springer, Cham (2017). Scholar
  18. 18.
    Yalniz, I.Z., Jégou, H., Chen, K., Paluri, M., Mahajan, D.: Billion-scale semi-supervised learning for image classification. arXiv preprint arXiv:1905.00546 (2019)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceAmerican University of BeirutBeirutLebanon
  2. 2.Department of OphthalmologyAmerican University of BeirutBeirutLebanon

Personalised recommendations