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Retinal-Layer Segmentation Using Dilated Convolutions

  • T. Guru Pradeep ReddyEmail author
  • Kandiraju Sai Ashritha
  • T. M. Prajwala
  • G. N. Girish
  • Abhishek R. Kothari
  • Shashidhar G. Koolagudi
  • Jeny Rajan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1022)

Abstract

Visualization and analysis of Spectral Domain Optical Coherence Tomography (SD-OCT) cross-sectional scans has gained a lot of importance in the diagnosis of several retinal abnormalities. Quantitative analytic techniques like retinal thickness and volumetric analysis are performed on cross-sectional images of the retina for early diagnosis and prognosis of retinal diseases. However, segmentation of retinal layers from OCT images is a complicated task on account of certain factors like speckle noise, low image contrast and low signal-to-noise ratio amongst many others. Owing to the importance of retinal layer segmentation in diagnosing ophthalmic diseases, manual segmentation techniques have been proposed and adopted in clinical practice. Nonetheless, manual segmentations suffer from erroneous boundary detection issues. This paper thus proposes a fully automated semantic segmentation technique that uses an encoder–decoder architecture to accurately segment the prominent retinal layers.

Keywords

Retinal layer segmentation Optical coherence tomography Dilated convolutions Deep learning Retina 

Notes

Acknowledgements

This work was supported by the Science and Engineering Research Board (Department of Science and Technology, India) through project funding EMR/2016/002677.

The authors would like to thank Vision and Image Processing (VIP) Lab, Department of Biomedical Engineering, Duke University, Durham, NC, USA for providing DME dataset.

References

  1. 1.
    Anger, E.M., Unterhuber, A., Hermann, B., Sattmann, H., Schubert, C., Morgan, J.E., Cowey, A., Ahnelt, P.K., Drexler, W.: Ultrahigh resolution optical coherence tomography of the monkey fovea. identification of retinal sublayers by correlation with semithin histology sections. Exp. Eye Res. 78(6), 1117–1125 (2004)Google Scholar
  2. 2.
    Bouma, B., Tearney, G., Yabushita, H., Shishkov, M., Kauffman, C., Gauthier, D.D., MacNeill, B., Houser, S., Aretz, H., Halpern, E.F., et al.: Evaluation of intracoronary stenting by intravascular optical coherence tomography. Heart 89(3), 317–320 (2003)CrossRefGoogle Scholar
  3. 3.
    Chiu, S.J., Allingham, M.J., Mettu, P.S., Cousins, S.W., Izatt, J.A., Farsiu, S.: Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema. Biomed. Opt. Express 6(4), 1172–1194 (2015)CrossRefGoogle Scholar
  4. 4.
    Chiu, S.J., Li, X.T., Nicholas, P., Toth, C.A., Izatt, J.A., Farsiu, S.: Automatic segmentation of seven retinal layers in sdoct images congruent with expert manual segmentation. Opt. Express 18(18), 19413–19428 (2010)CrossRefGoogle Scholar
  5. 5.
    Chiu, S.J., Toth, C.A., Rickman, C.B., Izatt, J.A., Farsiu, S.: Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming. Biomed. Opt. Express 3(5), 1127–1140 (2012)CrossRefGoogle Scholar
  6. 6.
    Coscas, G.: Optical Coherence Tomography in Age-Related Macular Degeneration. Springer Science & Business Media (2009)Google Scholar
  7. 7.
    De Boer, J.F., Cense, B., Park, B.H., Pierce, M.C., Tearney, G.J., Bouma, B.E.: Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography. Opt. Lett. 28(21), 2067–2069 (2003)CrossRefGoogle Scholar
  8. 8.
    Fu, T., Liu, X., Liu, D., Yang, Z.: A deep convolutional feature based learning layer-specific edges method for segmenting oct image. In: Ninth International Conference on Digital Image Processing (ICDIP 2017). vol. 10420, p. 1042029. International Society for Optics and Photonics (2017)Google Scholar
  9. 9.
    Girish, G.N., Thakur, B., Chowdhury, S.R., Kothari, A.R., Rajan, J.: Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model. IEEE J. Biomed. Health Inform. (2018)Google Scholar
  10. 10.
    Huang, D., Swanson, E.A., Lin, C.P., Schuman, et al.: Optical coherence tomography. Science 254(5035), 1178–1181 (1991)Google Scholar
  11. 11.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). arXiv preprint arXiv:1502.03167
  12. 12.
    Ramachandran, P., Zoph, B., Le, Q.V.: Searching for activation functions. CoRR (2017). arXiv preprint arXiv:1710.05941
  13. 13.
    Roy, A.G., Conjeti, S., Carlier, S.G., Dutta, P.K., Kastrati, A., Laine, A.F., Navab, N., Katouzian, A., Sheet, D.: Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks. IEEE J. Biomed. Health Inform. 20(2), 606–614 (2016)CrossRefGoogle Scholar
  14. 14.
    Roy, A.G., Conjeti, S., Carlier, S.G., Houissa, K., König, A., Dutta, P.K., Laine, A.F., Navab, N., Katouzian, A., Sheet, D.: Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE, pp. 1359–1362 (2016)Google Scholar
  15. 15.
    Roy, A.G., Conjeti, S., Karri, S.P.K., Sheet, D., Katouzian, A., Wachinger, C., Navab, N.: Relaynet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed. Opt. Express 8(8), 3627–3642 (2017)CrossRefGoogle Scholar
  16. 16.
    Yeh, A.T., Kao, B., Jung, W.G., Chen, Z., Nelson, J.S., Tromberg, B.J.: Imaging wound healing using optical coherence tomography and multiphoton microscopy in an in vitro skin-equivalent tissue model. J. Biomed. Opt. 9(2), 248–254 (2004)CrossRefGoogle Scholar
  17. 17.
    Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions (2015). arXiv preprint arXiv:1511.07122

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • T. Guru Pradeep Reddy
    • 1
    Email author
  • Kandiraju Sai Ashritha
    • 1
  • T. M. Prajwala
    • 1
  • G. N. Girish
    • 1
  • Abhishek R. Kothari
    • 2
  • Shashidhar G. Koolagudi
    • 1
  • Jeny Rajan
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of Technology KarnatakaSurathkalIndia
  2. 2.Pink City Eye and Retina CenterJaipurIndia

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