Skip to main content

Advertisement

SpringerLink
Log in

Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning

  • Retinal Disorders
  • Published:
Graefe's Archive for Clinical and Experimental Ophthalmology Aims and scope Submit manuscript

Abstract

Purpose

Our purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT).

Methods

A total of 1112 cross-section SD-OCT images of patients with exudative AMD and a healthy control group were used for this study. In the first step, an open-source multi-layer deep convolutional neural network (DCNN), which was pretrained with 1.2 million images from ImageNet, was trained and validated with 1012 cross-section SD-OCT scans (AMD: 701; healthy: 311). During this procedure training accuracy, validation accuracy and cross-entropy were computed. The open-source deep learning framework TensorFlow™ (Google Inc., Mountain View, CA, USA) was used to accelerate the deep learning process. In the last step, a created DCNN classifier, using the information of the above mentioned deep learning process, was tested in detecting 100 untrained cross-section SD-OCT images (AMD: 50; healthy: 50). Therefore, an AMD testing score was computed: 0.98 or higher was presumed for AMD.

Results

After an iteration of 500 training steps, the training accuracy and validation accuracies were 100%, and the cross-entropy was 0.005. The average AMD scores were 0.997 ± 0.003 in the AMD testing group and 0.9203 ± 0.085 in the healthy comparison group. The difference between the two groups was highly significant (p < 0.001).

Conclusions

With a deep learning-based approach using TensorFlow™, it is possible to detect AMD in SD-OCT with high sensitivity and specificity. With more image data, an expansion of this classifier for other macular diseases or further details in AMD is possible, suggesting an application for this model as a support in clinical decisions. Another possible future application would involve the individual prediction of the progress and success of therapy for different diseases by automatically detecting hidden image information.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Chou CF, Cotch MF, Vitale S, Zhang X, Klein R, Friedman DS, Klein BE, Saaddine JB (2013) Age-related eye diseases and visual impairment among U.S. adults. Am J Prev Med 45:29–35

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ferris FL 3rd, Wilkinson CP, Bird A, Chakravarthy U, Chew E, Csaky K, Sadda SR, Beckman Initiative for Macular Research Classification C (2013) Clinical classification of age-related macular degeneration. Ophthalmology 120:844–851

    Article  PubMed  Google Scholar 

  3. Huang D, Swanson E, Lin C, Schuman J, Stinson W, Chang W, Hee M, Flotte T, Gregory K, Puliafito C, Fujimoto J (1991) Optical coherence tomography. Science 254:1178–1181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Regatieri C, Branchini L, Duker J (2011) The role of spectral-domain OCT in the diagnosis and management of neovascular age-related macular degeneration. Ophthalmic Surg Lasers Imaging 42 Suppl:S56-66

  5. Lim E-H, Han J, Kim C, Cho S, Lee T (2013) Characteristic findings of optical coherence tomography in retinal Angiomatous proliferation. Korean J Ophthalmol 27:351–360

    Article  PubMed  PubMed Central  Google Scholar 

  6. Cicinelli MV, Rabiolo A, Sacconi R, Carnevali A, Querques L, Bandello F, Querques G (2017) Optical coherence tomography angiography in dry age-related macular degeneration. Surv Ophthalmol. https://doi.org/10.1016/j.survophthal.2017.06.005

  7. Rampasek L, Goldenberg A (2016) TensorFlow: Biology's gateway to deep learning? Cell systems 2(1):12–14. https://doi.org/10.1016/j.cels.2016.01.009

    Article  CAS  PubMed  Google Scholar 

  8. van Ginneken B (2017) Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning. Radiol Phys Technol 10:23–32

    Article  PubMed  PubMed Central  Google Scholar 

  9. Bogunovic H, Waldstein S, Schlegl T, Langs G, Sadeghipour A, Liu X, Gerendas B, Osborne A, Schmidt-Erfurth U (2017) Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach. Invest Ophthalmol Vis Sci 58:3240–4248

    Article  PubMed  Google Scholar 

  10. ElTanboly A, Ismail M, Shalaby A, Switala A, El-Baz A, Schaal S, Gimel'farb G, El-Azab M (2017) A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images. Med Phys 44:914–923

    Article  CAS  PubMed  Google Scholar 

  11. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402–2410

    Article  PubMed  Google Scholar 

  12. Abramoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57:5200–5206

    Article  PubMed  Google Scholar 

  13. Burlina P, Pacheco KD, Joshi N, Freund DE, Bressler NM (2017) Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis. Comput Biol Med 82:80–86

    Article  PubMed  Google Scholar 

  14. Wang Y, Zhang Y, Yao Z, Zhao R, Zhou F (2017) Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images. Biomed Opt Express 7:4928–4940

    Article  Google Scholar 

  15. Gao S, Patel R, Jain N, Zhang M, Weleber R, Huang D, Pennesi M, Jia Y (2016) Choriocapillaris evaluation in choroideremia using optical coherence tomography angiography. Biomed Opt Express 8:48–56

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gerendas BS, Bogunovic H, Sadeghipour A, Schlegl T, Langs G, Waldstein SM, Schmidt-Erfurth U (2017) Computational image analysis for prognosis determination in DME. Vis Res. https://doi.org/10.1016/j.visres.2017.03.008

  17. Venhuizen F, van Ginneken B, van Asten F, van Grinsven M, Fauser S, Hoyng C, Theelen T, Sánchez C (2017) Automated staging of age-related macular degeneration using optical coherence tomography. Invest Ophthalmol Vis Sci 58:2318–2328

    Article  PubMed  Google Scholar 

  18. Miri MS, Abramoff MD, Kwon YH, Sonka M, Garvin MK (2017) A machine-learning graph-based approach for 3D segmentation of Bruch's membrane opening from glaucomatous SD-OCT volumes. Med Image Anal 39:206–217

    Article  PubMed  Google Scholar 

  19. Alsaih K, Lemaitre G, Rastgoo M, Massich J, Sidibe D, Meriaudeau F (2017) Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. Biomed Eng Online 16:68

    Article  PubMed  PubMed Central  Google Scholar 

  20. Waldstein SM, Montuoro A, Podkowinski D, Philip AM, Gerendas BS, Bogunovic H, Schmidt-Erfurth U (2017) Evaluating the impact of vitreomacular adhesion on anti-VEGF therapy for retinal vein occlusion using machine learning. Sci Rep 7:2928

    Article  PubMed  PubMed Central  Google Scholar 

  21. Vogl W, Waldstein S, Gerendas B, Schmidt-Erfurth U, Langs G (2017) Predicting macular edema recurrence from Spatio-Temporal signatures in optical coherence tomography images. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2017.2700213

  22. Montuoro A, Waldstein S, Gerendas B, Schmidt-Erfurth U, Bogunović H (2017) Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context. Biomed Opt Express 8:1874–1888

    Article  PubMed  PubMed Central  Google Scholar 

  23. Kim S, Cho K, Oh S (2017) Development of machine learning models for diagnosis of glaucoma. PLoS One 12:e0177726

    Article  PubMed  PubMed Central  Google Scholar 

  24. Murugeswari S, Sukanesh R (2017) Investigations of severity level measurements for diabetic macular oedema using machine learning algorithms. Ir J Med Sci. https://doi.org/10.1007/s11845-017-1598-8

  25. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado G, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mane′ D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Vie′ gas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: Large-scale machine learning on heterogeneous distributed systems. TensorFlow. https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf. Accessed 4 June 2017

  26. Szegedy C, Vanhoucke V, Ioffe S, Shlens J (2016) Rethinking the inception architecture for computer vision. IEEE Conf Comput Vis Pattern Recognit (CVPR) 2016:2818–2826

    Google Scholar 

  27. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) ImageNet- a large-scale hierarchical image database. CVPR 2009 - IEEE Conf Comput Vis Pattern Recognit 2009:248–255

    Article  Google Scholar 

  28. TensorFlow (2017) http://www.tensorflow.org/tutorials/image_recognition. TensorFlow. Accessed 26 June 2017

  29. Google Developers (2017) https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0. Google Developers. Accessed 4 July 2017

  30. Angermueller C, Parnamaa T, Parts L, Stegle O (2016) Deep learning for computational biology. Mol Syst Biol 12:878

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using Convolutional neural networks. Radiology 284:574–582

    Article  PubMed  Google Scholar 

Download references

Funding

No funding was received for this research.

Author information

Authors and Affiliations

  1. Department of Ophthalmology, University of Muenster Medical Center, Domagkstraße 15, 48149, Muenster, Germany

    Maximilian Treder, Jost Lennart Lauermann & Nicole Eter

Authors
  1. Maximilian Treder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jost Lennart Lauermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicole Eter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maximilian Treder.

Ethics declarations

Conflict of interest

All authors certify that they have no affiliations

with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Financial disclosure

M. Treder: Allergan, Novartis; J.L. Lauermann: Bayer, Novartis; and N. Eter: Allergan, Alimera, Bausch and Lomb, Bayer, Heidelberg Engineering, Novartis, Roche.

Ethical approval

All procedures performed in studies involving human

participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

For this type of study formal consent is not required.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Treder, M., Lauermann, J.L. & Eter, N. Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning. Graefes Arch Clin Exp Ophthalmol 256, 259–265 (2018). https://doi.org/10.1007/s00417-017-3850-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00417-017-3850-3

Keywords

Search

Navigation