CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography

  • Shekoufeh Gorgi Zadeh
  • Maximilian W. M. Wintergerst
  • Vitalis Wiens
  • Sarah Thiele
  • Frank G. Holz
  • Robert P. Finger
  • Thomas Schultz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)

Abstract

Optical coherence tomography (OCT) is used to diagnose and track progression of age-related macular degeneration (AMD). Drusen, which appear as bumps between Bruch’s membrane (BM) and the retinal pigment epithelium (RPE) layer, are among the most important biomarkers for staging AMD. In this work, we develop and compare three automated methods for Drusen segmentation based on the U-Net convolutional neural network architecture. By cross-validating on more than 50, 000 annotated images, we demonstrate that all three approaches achieve much better accuracy than a current state-of-the-art method. Highest accuracy is achieved when the CNN is trained to segment the BM and RPE, and the drusen are detected by combining shortest path finding with polynomial fitting in a post-process.

References

  1. 1.
    Abràmoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)CrossRefGoogle Scholar
  2. 2.
    Chen, Q., Leng, T., Zheng, L., Kutzscher, L., Ma, J., de Sisternes, L., Rubin, D.L.: Automated drusen segmentation and quantification in SD-OCT images. Med. Image Anal. 17(8), 1058–1072 (2013)CrossRefGoogle Scholar
  3. 3.
    Chiu, S.J., Izatt, J.A., O’Connell, R.V., Winter, K.P., Toth, C.A., Farsiu, S.: Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images. Invest. Opthalmol. Vis. Sci. 53(1), 53 (2012)CrossRefGoogle Scholar
  4. 4.
    Farsiu, S., Chiu, S.J., Izatt, J.A., Toth, C.A.: Fast detection and segmentation of drusen in retinal optical coherence tomography images. In: Proceedings of SPIE, Ophthalmic Technologies XVIII, vol. 6844, p. 68440D (2008)Google Scholar
  5. 5.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
  6. 6.
    Iwama, D., Hangai, M., Ooto, S., Sakamoto, A., Nakanishi, H., Fujimura, T., Domalpally, A., Danis, R.P., Yoshimura, N.: Automated assessment of drusen using three-dimensional spectral-domain optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 53(3), 1576–1583 (2012)CrossRefGoogle Scholar
  7. 7.
    Jager, R.D., Mieler, W.F., Miller, J.W.: Age-related macular degeneration. New Engl. J. Med. 358(24), 2606–2617 (2008)CrossRefGoogle Scholar
  8. 8.
    Jain, N., Farsiu, S., Khanifar, A.A., Bearelly, S., Smith, R.T., Izatt, J.A., Toth, C.A.: Quantitative comparison of drusen segmented on SD-OCT versus drusen delineated on color fundus photographs. Invest. Ophthalmol. Vis. Sci. 51(10), 4875–4883 (2010)CrossRefGoogle Scholar
  9. 9.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
  10. 10.
    Lee, C.S., Baughman, D.M., Lee, A.Y.: Deep learning is effective for classifying normal versus age-related macular degeneration optical coherence tomography images. Ophthalmol. Retina 1(4), 322–327 (2017)Google Scholar
  11. 11.
    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). doi: 10.1007/978-3-319-24574-4_28 CrossRefGoogle Scholar
  12. 12.
    Schmidt-Erfurth, U., Bogunovic, H., Klimscha, S., Hu, X., Schlegl, T., Sadeghipour, A., Gerendas, B.S., Osborne, A., Waldstein, S.M.: Machine learning to predict the individual progression of AMD from imaging biomarkers. In: Proceedings of Association for Research in Vision and Ophthalmology, p. 3398 (2017)Google Scholar
  13. 13.
    de Sisternes, L., Simon, N., Tibshirani, R., Leng, T., Rubin, D.L.: Quantitative SD-OCT imaging biomarkers as indicators of age-related macular degeneration progressionpredicting AMD progression using SD-OCT features. Invest. Ophthalmol. Vis. Sci. 55(11), 7093–7103 (2014)CrossRefGoogle Scholar
  14. 14.
    Sonka, M., Abràmoff, M.D.: Quantitative analysis of retinal OCT. Med. Image Anal. 33, 165–169 (2016)CrossRefGoogle Scholar
  15. 15.
    Zheng, Y., Williams, B.M., Pratt, H., Al-Bander, B., Wu, X., Zhao, Y.: Computer aided diagnosis of age-related macular degeneration in 3D OCT images by deep learning. In: Proceedings of Association for Research in Vision and Ophthalmology, p. 824 (2017)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shekoufeh Gorgi Zadeh
    • 1
  • Maximilian W. M. Wintergerst
    • 2
  • Vitalis Wiens
    • 1
  • Sarah Thiele
    • 2
  • Frank G. Holz
    • 2
  • Robert P. Finger
    • 2
  • Thomas Schultz
    • 1
  1. 1.Department of Computer ScienceUniversity of BonnBonnGermany
  2. 2.Department of OphthalmologyUniversity of BonnBonnGermany

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