Abstract
The automated diagnosis of ophthalmologic diseases to assist the medical ophthalmologist in their daily practice is the subject of much research. Recently, image processing based on very deep and complex processing structures became the focus of renewed interest, mostly as a result of excellent performance in a wide range of problems. One of the main drivers of this interest in these structures, convolutional neural networks (CNNs), is the availability of fast and highly parallel hardware, enabling the fast training and efficient use of these structures. In this chapter, we briefly describe the major characteristics of CNNs and discuss the anatomy and physiology of the eye and common ocular diseases. A review of the state-of-the-art use of CNNs in the diagnosis of common eye diseases is then presented. The selection of the works reviewed followed the criteria of utility, recency and quality in order to assemble a set of representative works. An original contribution reporting the use of CNNs to quantify some corneal endothelial morphometric parameters is then presented in a separate section. Finally, some considerations are made on possible developments of the techniques described, as made possible by the evolution of computing technology.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abràmoff MD, Folk JC, Han DP, Walker JD, Williams DF, Russell SR et al (2013) Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol 131:351–357. http://dx.doi.org/10.1001/jamaophthalmol.2013.1743
Abràmoff 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(13):5200–5206. http://dx.doi.org/10.1167/iovs.16-19964
Age-Related Eye Disease Study Research Group (2001) The Age-Related Eye Disease Study system for classifying age-related macular degeneration from stereoscopic color fundus photographs: the Age-Related Eye Disease Study Report Number 6. Am J Ophthalmol 132(6):668–681. https://doi.org/S0002939401012181
Benson WE, Blodi BA, Boldt HC, Murray TG, Regillo CD, Scott IU (2008) Age-related macular degeneration, preferred practice pattern. Am Acad Ophthalmol
Bourne WM (2003) Biology of the corneal endothelium in health and disease. Eye (London, England) 17(8):912–8. http://dx.doi.org/10.1038/sj.eye.6700559
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. http://dx.doi.org/10.1016/j.compbiomed.2017.01.018
Cheriguene S, Azizi N, Zemmal N, Dey N, Djellali H, Farah N (2015) Optimized tumor breast cancer classification using combining random subspace and static classifiers selection paradigms. In Hassanien A-E, Grosan C, Tolba MF (eds) Applications of intelligent optimization in biology and medicine—intelligent systems reference library, vol 96. Springer, Berlin, pp 289–307
Chhablani J, Barteselli G, Wang H, El-Emam S, Kozak I, Doede AL et al (2012) Repeatability and reproducibility of manual choroidal volume measurements using enhanced depth imaging optical coherence tomography. Invest Ophthalmol Vis Sci 53(4):2274–2280. http://dx.doi.org/10.1167/iovs.12-9435
Eadie LH, Taylor P, Gibson AP (2012) A systematic review of computer-assisted diagnosis in diagnostic cancer imaging. Eur J Radiol 81(1):e70–e76. http://dx.doi.org/10.1016/j.ejrad.2011.01.098
Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012) An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 59(9):2538–2548. http://dx.doi.org/10.1109/TBME.2012.2205687
Gao X, Lin S, Wong TY (2015) Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans Biomed Eng 62(11):2693–2701. http://dx.doi.org/10.1109/TBME.2015.2444389
Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 1–8. http://dx.doi.org/10.1016/j.ophtha.2017.02.008
Giancardo L, Member S, Meriaudeau F, Karnowski TP, Li Y, Jr Tobin KW et al (2011) Microaneurysm detection with radon transform-based classification on retina images pp 5939–5942
Goodfellow I, Pouget-Abadie J, Mirza M (2014) Generative adversarial networks. arXiv Preprint arXiv: …, 1–9. Retrieved from http://arxiv.org/abs/1406.2661
Haloi M (2015) Improved microaneurysm detection using deep neural networks. arXiv:1505.04424 [Cs]
Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. NIPS 2014 Deep Learning Workshop, 1–9. http://dx.doi.org/10.1063/1.4931082
Hoover AD, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging, 19(3):203–210. http://dx.doi.org/10.1109/42.845178
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556v6, 1–14
Kauppi T, Kalesnykiene V, Kamarainen J-K, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Klviinen H, Pietil J (2007) DIARETDB1 diabetic retinopathy database and evaluation protocol. In Proceeding of the 11th Conference on Medical Image Understanding and Analysis. Aberystwyth, Wales, 2007
Kausar N, Abdullah A, Brahim S, Dey N (2014) Ensemble clustering algorithm with supervised classification of clinical data for early diagnosis of coronary artery disease. J Med Imaging Health Informatics, (December). http://dx.doi.org/10.1166/jmihi.2016.1593
Kim Y-D, Park E, Yoo S, Choi T, Yang L, Shin D (2015) Compression of deep convolutional neural networks for fast and low power mobile applications, 1–16. Retrieved from http://arxiv.org/abs/1511.06530
Krachmer JH, Mannis JM, Holland JE (2010) Cornea—Fundamentals diagnosis and management, 3rd edn. Mosby, Maryland Heights
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, 1–9. http://dx.doi.org/10.1016/j.protcy.2014.09.007
Li Q, Feng B, Xie L, Liang P, Zhang H, Wang T (2016) A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 35(1):109–118. http://dx.doi.org/10.1109/TMI.2015.2457891
Liu X, Jiang J, Zhang K, Long E, Cui J, Zhu M et al (2017) Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. Plos One 12(3):e0168606. http://dx.doi.org/10.1371/journal.pone.0168606
Maninis KK, Pont-Tuset J, Arbeláez P, Van Gool L (2016) Deep retinal image understanding. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Melinscak M, Prentasic P, Loncaric S (2015) Retinal vessel segmentation using deep neural networks. In: International Conference on Computer Vision Theory and Applications (VISAPP 2015), pp 577–582
Mitchell TM (1997) Machine learning, 1st ed. McGraw-Hill Education, New York
Mrejen S, Spaide RF (2013) Optical coherence tomography: imaging of the choroid and beyond. Surv Ophthalmol 58(5):387–429. http://dx.doi.org/10.1016/j.survophthal.2012.12.001
Olsen TW, Adelman RA, Flaxel CJ, Folk JC, Pulido JS, Regilo CD, Hyman L (2016) Preferred practice pattern: diabetic retinopathy. Am Acad Ophthalmol http://www.aao.org/preferred-practice-pattern/diab. http://dx.doi.org/10.1016/S0140-6736(09)62124-3
Papernot N, McDaniel P, Jha S, Fredrikson M, Celik ZB, Swami A (2015) The limitations of deep learning in adversarial settings. In: IEEE European Symposium on Security & Privacy, IEEE 2016, Saarbrucken, Germany. arXiv:1511.07528. http://dx.doi.org/10.1109/EuroSP.2016.36
Prentasic P, Loncaric S (2016) Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Comput Methods Programs Biomed 137:281–292. http://dx.doi.org/10.1016/j.cmpb.2016.09.018
Prentasic P, Loncaric S, Vatavuk Z, Bencic G, Subasic M, Petkovic T et al (2013) Diabetic Retinopathy Image Database(DRiDB): a new database for diabetic retinopathy screening programs research. In: 2013 8th International Symposium on Image and Signal Processing and Analysis (Ispa), vol 711, pp 711–716
Ravi D, Wong C, Deligianni F, Berthelot M, Andreu Perez J, Lo B, Yang G-Z (2016) Deep learning for health informatics. IEEE J Biomed Health Inf 21(1):1–1. http://dx.doi.org/10.1109/JBHI.2016.2636665
Roychowdhury S, Koozekanani D, Parhi K (2013) DREAM: diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Inf PP(99), 1. http://dx.doi.org/10.1109/JBHI.2013.2294635
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536. http://dx.doi.org/10.1038/323533a0
Russell S, Norvig P (2003) Artifitial intelligence—A modern approach, 2nd edn. Prentice Hall, New Jersey
Selig B, Vermeer KA, Rieger B, Hillenaar T, Luengo Hendriks CL (2015) Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy. BMC Med Imaging 15(1):13. http://dx.doi.org/10.1186/s12880-015-0054-3
Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2013) OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv Preprint arXiv, 1312.6229. Retrieved from http://arxiv.org/abs/1312.6229
Sharma K, Virmani J (2017) A decision support system for classification of normal and medical renal disease using ultrasound images: a decision support system for medical renal diseases. Int J Ambient Comput Intell (IJACI) 8(2). https://doi.org/10.4018/IJACI.2017040104
Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2005) Ridge based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509. http://dx.doi.org/10.1109/TMI.2004.825627
Sui X, Zhang S, Wei B, Bi H, Wu J, Pan X et al (2017) Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks. Neurocomputing (January), 0–1. http://dx.doi.org/10.1016/j.neucom.2017.01.023
Trucco E, Ruggeri A, Karnowski T, Giancardo L, Chaum E, Hubschman JP et al (2017) Validating retinal fundus image analysis algorithms : issues and a proposal. IOVS. http://dx.doi.org/10.1167/iovs.12-10347
Yanoff M, Duker JS (2014). Ophthalmology. In Ophtalmolgy, 4th edn. Saunders, Elsevier
Zhanga Y, Zhanga B, Lua W (2011) Breast cancer classification from histological images with multiple features and random subspace classifier ensemble. In: AIP Conference Proceedings, vol 19(1371). http://dx.doi.org/10.1063/1.3596623
Zilly J, Buhmann JM, Mahapatra D (2017) Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput Med Imaging Graph 55:28–41. http://dx.doi.org/10.1016/j.compmedimag.2016.07.012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Galveia, J.N., Travassos, A., Quadros, F.A., da Silva Cruz, L.A. (2018). Computer Aided Diagnosis in Ophthalmology: Deep Learning Applications. In: Dey, N., Ashour, A., Borra, S. (eds) Classification in BioApps. Lecture Notes in Computational Vision and Biomechanics, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-65981-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-65981-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65980-0
Online ISBN: 978-3-319-65981-7
eBook Packages: EngineeringEngineering (R0)