U-Net with Spatial Pyramid Pooling for Drusen Segmentation in Optical Coherence Tomography

  • Rhona AsgariEmail author
  • Sebastian Waldstein
  • Ferdinand Schlanitz
  • Magdalena Baratsits
  • Ursula Schmidt-Erfurth
  • Hrvoje Bogunović
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11855)


The presence of drusen is the main hallmark of early/intermediate age-related macular degeneration (AMD). Therefore, automated drusen segmentation is an important step in image-guided management of AMD. There are two common approaches to drusen segmentation. In the first, the drusen are segmented directly as a binary classification task. In the second approach, the surrounding retinal layers (outer boundary retinal pigment epithelium (OBRPE) and Bruch’s membrane (BM)) are segmented and the remaining space between these two layers is extracted as drusen. In this work, we extend the standard U-Net architecture with spatial pyramid pooling components to introduce global feature context. We apply the model to the task of segmenting drusen together with BM and OBRPE. The proposed network was trained and evaluated on a longitudinal OCT dataset of 425 scans from 38 patients with early/intermediate AMD. This preliminary study showed that the proposed network consistently outperformed the standard U-net model.



This work was funded by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development. We thank the NVIDIA corporation for a GPU donation.


  1. 1.
    Wong, W.L., et al.: Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob. health 2(2), e106–e116 (2014)CrossRefGoogle Scholar
  2. 2.
    Schlanitz, F.G., et al.: Drusen volume development over time and its relevance to the course of age-related macular degeneration. Br. J. Ophthalmol. 101(2), 198–203 (2017)CrossRefGoogle Scholar
  3. 3.
    Gorgi Zadeh, S., et al.: CNNs enable accurate and fast segmentation of drusen in optical coherence tomography. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 65–73. Springer, Cham (2017). Scholar
  4. 4.
    Khalid, S., Akram, M.U., Hassan, T., Jameel, A., Khalil, T.: Automated segmentation and quantification of Drusen in Fundus and optical coherence tomography images for detection of ARMD. J. Digit. Imaging 31(4), 464–476 (2018). Scholar
  5. 5.
    Novosel, J., Vermeer, K.A., de Jong, J.H., Wang, Z., van Vliet, L.J.: Joint segmentation of retinal layers and focal lesions in 3-D OCT data of topologically disrupted retinas. IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017) CrossRefGoogle Scholar
  6. 6.
    Fang, L., et al.: Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed. Opt. Express 8(5), 2732–2744 (2017)CrossRefGoogle Scholar
  7. 7.
    Shah, A., et al.: Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images. Biomedical Optics Express 9(9), 4509–4526 (2018)CrossRefGoogle Scholar
  8. 8.
    Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)Google Scholar
  9. 9.
    He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. CoRR abs/1406.4729 (2014)CrossRefGoogle Scholar
  10. 10.
    Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
  11. 11.
    Gu, Z., et al.: DeepDisc: optic disc segmentation based on atrous convolution and spatial pyramid pooling. In: Stoyanov, D., et al. (eds.) OMIA/COMPAY -2018. LNCS, vol. 11039, pp. 253–260. Springer, Cham (2018). Scholar
  12. 12.
    Zhao, R., et al.: Automated Drusen detection in dry age-related macular degeneration by multiple-depth, en face optical coherence tomography. Biomed. Opt. Express 8(11), 5049 (2017)CrossRefGoogle Scholar
  13. 13.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  14. 14.
    Crum, W.R., Camara, O., Hill, D.L.G.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans. Med. Imaging 25(11), 1451–1461 (2006). NovCrossRefGoogle Scholar
  15. 15.
    Chen, X., Niemeijer, M., Zhang, L., Lee, K., Abràmoff, M.D., Sonka, M.: Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut. IEEE-TMI 31(8), 1521–1531 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rhona Asgari
    • 1
    Email author
  • Sebastian Waldstein
    • 1
  • Ferdinand Schlanitz
    • 1
  • Magdalena Baratsits
    • 1
  • Ursula Schmidt-Erfurth
    • 1
  • Hrvoje Bogunović
    • 1
  1. 1.Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of OphthalmologyMedical University of ViennaViennaAustria

Personalised recommendations