Skip to main content

A Deep Learning Approach for Semantic Segmentation of Gonioscopic Images to Support Glaucoma Categorization

  • Conference paper
  • First Online:
Medical Image Understanding and Analysis (MIUA 2020)

Abstract

We present a deep learning semantic segmentation algorithm for processing images acquired by a novel ophthalmic device, the NIDEK GS-1. The proposed model can sophisticate the current reference exam, called gonioscopy, for evaluating the risk of developing glaucoma, a severe eye pathology with a considerable worldwide impact in terms of costs and negative effects on affected people’s quality of life, and for inferring its categorization. The target eye region of gonioscopy is the interface between the iris and the cornea, and the anatomical structures that are located there. Our approach exploits a dense U-net architecture and is the first automatic system segmenting irido-corneal interface images from the novel device. Results show promising performance, providing about 88% of mean pixel-wise classification accuracy in a 5-fold cross-validation experiment on a very limited size dataset of annotated images.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abràmoff, M.D., et al.: Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest. Opthalmol. Vis. Sci. 57(13), 5200 (2016). https://doi.org/10.1167/iovs.16-19964

    Article  Google Scholar 

  2. Alward, W.: Color Atlas of Gonioscopy. American Academy of Ophthalmology, San Francisco (2008)

    Google Scholar 

  3. Ben-Cohen, A., et al.: Retinal layers segmentation using fully convolutional network in OCT images (2017)

    Google Scholar 

  4. Chandra, A., Gupta, A., Gupta, V., Sihota, R., Azad, R., Chandra, P.: Comparative evaluation of RetCam vs. gonioscopy images in congenital glaucoma. Indian J. Ophthalmol. 62(2), 163 (2014). https://doi.org/10.4103/0301-4738.116487

    Article  Google Scholar 

  5. Cheng, J., et al.: Closed angle glaucoma detection in RetCam images. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE, August 2010. https://doi.org/10.1109/iembs.2010.5627290

  6. Dutta, A., Zisserman, A.: The VIA annotation software for images, audio and video. In: Proceedings of the 27th ACM International Conference on Multimedia, MM 2019. ACM, New York (2019). https://doi.org/10.1145/3343031.3350535

  7. Fang, L., Cunefare, D., Wang, C., Guymer, R.H., Li, S., Farsiu, S.: 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 (2017). https://doi.org/10.1364/boe.8.002732

    Article  Google Scholar 

  8. Fauw, J.D., et al.: Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24(9), 1342–1350 (2018). https://doi.org/10.1038/s41591-018-0107-6

    Article  Google Scholar 

  9. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: The IEEE International Conference on Computer Vision (ICCV), December 2015

    Google Scholar 

  10. Heckbert, P.: Graphics Gems IV. AP Professional, Boston (1994)

    MATH  Google Scholar 

  11. Hertzog, L.H., Albrecht, K.G., LaBree, L., Lee, P.P.: Glaucoma care and conformance with preferred practice patterns. Ophthalmology 103(7), 1009–1013 (1996). https://doi.org/10.1016/s0161-6420(96)30573-3

    Article  Google Scholar 

  12. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017). https://doi.org/10.1109/cvpr.2017.243

  13. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning, ICML 2015, vol. 37, pp. 448–456 (2015). JMLR.org

  14. Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017

    Google Scholar 

  15. Li, Z., He, Y., Keel, S., Meng, W., Chang, R.T., He, M.: Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 125(8), 1199–1206 (2018). https://doi.org/10.1016/j.ophtha.2018.01.023

    Article  Google Scholar 

  16. Liu, X., et al.: Semi-supervised automatic segmentation of layer and fluid region in retinal optical coherence tomography images using adversarial learning. IEEE Access 7, 3046–3061 (2019). https://doi.org/10.1109/access.2018.2889321

    Article  Google Scholar 

  17. Orlando, J.I., et al.: U2-Net: a Bayesian u-net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, April 2019. https://doi.org/10.1109/isbi.2019.8759581

  18. Pead, E., et al.: Automated detection of age-related macular degeneration in color fundus photography: a systematic review. Surv. Ophthalmol. 64(4), 498–511 (2019). https://doi.org/10.1016/j.survophthal.2019.02.003

    Article  Google Scholar 

  19. Pekala, M., Joshi, N., Liu, T.A., Bressler, N., DeBuc, D.C., Burlina, P.: Deep learning based retinal OCT segmentation. Comput. Biol. Med. 114, 103445 (2019). https://doi.org/10.1016/j.compbiomed.2019.103445

    Article  Google Scholar 

  20. Raghavendra, U., Fujita, H., Bhandary, S.V., Gudigar, A., Tan, J.H., Acharya, U.R.: Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Inf. Sci. 441, 41–49 (2018). https://doi.org/10.1016/j.ins.2018.01.051

    Article  MathSciNet  Google Scholar 

  21. 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). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  22. Roy, A.G., et al.: ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed. Opt. Express 8(8), 3627–3642 ( 2017). https://doi.org/10.1364/BOE.8.003627. http://www.osapublishing.org/boe/abstract.cfm?URI=boe-8-8-3627

  23. Tham, Y.C., Li, X., Wong, T.Y., Quigley, H.A., Aung, T., Cheng, C.Y.: Global prevalence of glaucoma and projections of glaucoma burden through 2040. Ophthalmology 121(11), 2081–2090 (2014). https://doi.org/10.1016/j.ophtha.2014.05.013

    Article  Google Scholar 

  24. Trucco, E., et al.: Validating retinal fundus image analysis algorithms: issues and a proposal. Invest. Opthalmol. Vis. Sci. 54(5), 3546 (2013). https://doi.org/10.1167/iovs.12-10347

    Article  Google Scholar 

Download references

Acknowledgement

We thank the CVIP/VAMPIRE research team of the University of Dundee, Dundee (UK) and the VAMPIRE research team of the University of Edinburgh, Edinburgh (UK) for useful discussions and suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrea Peroni .

Editor information

Editors and Affiliations

Ethics declarations

This work is fully funded by a Ph.D. studentship from NIDEK Technologies Srl.

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Peroni, A. et al. (2020). A Deep Learning Approach for Semantic Segmentation of Gonioscopic Images to Support Glaucoma Categorization. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-52791-4_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-52790-7

  • Online ISBN: 978-3-030-52791-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics