Deep Learning in Retinal Image Analysis: A Review
DOI:
https://doi.org/10.14738/jbemi.63.9492Keywords:
Retinal Imaging, Retinal Image Analysis, Computer aided diagnosis, Systemic ReviewAbstract
Retinal image analysis holds an imperative position for the identification and classification of retinal diseases such as Diabetic Retinopathy, Hypertensive Retinopathy, Glacuma, Age Related Macular Degeneration, Retinal Detachment, and other retinal disease. Automated identification of retinal diseases is a big step towards early diagnosis and prevention of exacerbation of the disease. A number of state-of-the-art methods have been developed in the past that helped in the automatic segmentation and identification of retinal landmarks and pathology. However, the current unprecedented advancements in deep learning and modern imaging modalities in ophthalmology have opened a whole new arena for researchers. This paper is a review of deep learning techniques applied to 2-D fundus and 3D-OCT retinal images for automated identification of retinal landmarks, pathology, and disease classification. The methodologies are analyzed in terms of sensitivity, specificity, Area under ROC curve, accuracy, and F score on publically available datasets which include DRIVE, STARE, CHASE-DB1, DRiDB, NIH AREDS, ARIA, MESSIDOR-2, E-OPTHA, EyePACS-1 DIARETDB and OCT image datasets.
References
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(38) Welikala, R.A., et al., Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies. Computers in biology and medicine, 2016. 71: p. 67-76.
(39) Owen, C.G., et al., Retinal Vasculometry Associations with Cardiometabolic Risk Factors in the European Prospective Investigation of Cancer—Norfolk Study. Ophthalmology, 2018.
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(41) Raman, V., P. Then, and P. Sumari. Proposed retinal abnormality detection and classification approach: Computer aided detection for diabetic retinopathy by machine learning approaches. in Communication Software and Networks (ICCSN), 2016 8th IEEE International Conference on. 2016. IEEE.
(42) Welikala, R.A., et al., Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy. Computerized Medical Imaging and Graphics, 2015. 43(0): p. 64-77.
(43) Engerman, R.L., Pathogenesis of diabetic retinopathy. Diabetes, 1989. 38(10): p. 1203-1206.
(44) Kowluru, R.A. and P.-S. Chan, Capillary dropout in diabetic retinopathy, in Diabetic Retinopathy. 2008, Springer. p. 265-282.
(45) Kanski, J.J. and B. Bowling, Clinical ophthalmology: a systematic approach. 2011: Elsevier Health Sciences.
(46) Klein, R., et al., The Wisconsin Epidemiologic Study of Diabetic Retinopathy XXIII: the twenty-five-year incidence of macular edema in persons with type 1
diabetes. Ophthalmology, 2009. 116(3): p. 497-503.
(47) Varma, R., et al., Prevalence of and risk factors for diabetic macular edema in the United States. JAMA ophthalmology, 2014. 132(11): p. 1334-1340.
(48) Jager, R.D., W.F. Mieler, and J.W. Miller, Age-related macular degeneration. New England Journal of Medicine, 2008. 358(24): p. 2606-2617.
(49) Alfaro, D.V., Age-related macular degeneration: a comprehensive textbook. 2006: Lippincott Williams & Wilkins.
(50) Fraz, M.M., et al. Retinal vessel segmentation using ensemble classifier of bagged decision trees. in IET Conference Publications. 2012. IEE.
(51) Fraz, M.M., et al., Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification. International Journal of Computer
Assisted Radiology and Surgery, 2014. 9(5): p. 795-811.
(52) Liew, G. and J.J. Wang, Retinal vascular signs: a window to the heart? Revista Española de Cardiología (English Edition), 2011. 64(6): p. 515-521.
(53) Quigley, H.A. and A.T. Broman, The number of people with glaucoma worldwide in 2010 and 2020. British journal of ophthalmology, 2006. 90(3): p. 262-267.
(54) Zahoor, M.N. and M.M. Fraz, A Correction to the Article “Fast Optic Disc Segmentation in Retina Using Polar Transform”. IEEE Access, 2018. 6: p. 4845-4849.
(55) Fraz, M.M., et al., Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification. International Journal of Computer Assisted Radiology and Surgery, 2014. 9(5): p. 795-811.
(56) Thomas, R. and R.S. Parikh, How to assess a patient for glaucoma. Community eye health, 2006. 19(59): p. 36.
(57) Goodfellow, I., Y. Bengio, and A. Courville, Deep learning. 2016: MIT press.
(58) Hsu, F.-H., Behind Deep Blue: Building the computer that defeated the world chess champion. 2002: Princeton University Press.
(59) LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. Nature, 2015. 521(7553): p. 436-444.
(60) Angelov, P. and A. Sperduti. Challenges in deep learning. in Proceedings of the 24th European symposium on artificial neural networks (ESANN). 2016.
(61) Wang, S., et al., Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing, 2015. 149: p. 708-717.
(62) Fang, T., et al. Retinal vessel landmark detection using deep learning and hessian matrix. in Image and Signal Processing (CISP), 2015 8th International
Congress on. 2015. IEEE.
(63) Maji, D., et al. Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images. in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. 2015. IEEE.
(64) Melinščak, M., P. Prentašić, and S. Lončarić. Retinal vessel segmentation using deep neural networks. in VISAPP 2015 (10th International Conference on Computer Vision Theory and Applications). 2015.
(65) Fu, H., et al. Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. in Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on. 2016. IEEE.
(66) Fu, H., et al. DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field. in International Conference on Medical Image Computing and Computer-Assisted Intervention. 2016. Springer.
(67) Liskowski, P. and K. Krawiec, Segmenting Retinal Blood Vessels With<? Pub _newline?> Deep Neural Networks. IEEE transactions on medical imaging, 2016. 35(11): p. 2369-2380.
(68) Li, Q., et al., A cross-modality learning approach for vessel segmentation in retinal images. IEEE transactions on medical imaging, 2016. 35(1): p. 109-118.
(69) Yao, Z., Z. Zhang, and L.-Q. Xu. Convolutional Neural Network for Retinal Blood Vessel Segmentation. in Computational Intelligence and Design (ISCID), 2016 9th International Symposium on. 2016. IEEE.
(70) Dasgupta, A. and S. Singh, A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation. arXiv preprint arXiv:1611.02064, 2016.
(71) Maninis, K.-K., et al. Deep retinal image understanding. in International Conference on Medical Image Computing and Computer-Assisted Intervention. 2016. Springer.
(72) Tan, J.H., et al., Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. Journal of Computational Science, 2017.
(73) Prentašić, P. and S. Lončarić. Detection of exudates in fundus photographs using convolutional neural networks. in Image and Signal Processing and Analysis (ISPA), 2015 9th International Symposium on. 2015. IEEE.
(74) Prentašić, P. and S. Lončarić, Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Computer Methods and Programs in Biomedicine, 2016. 137: p. 281-292.
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Published
2019-06-29
How to Cite
King, T. ., & Ayuba, A. . (2019). Deep Learning in Retinal Image Analysis: A Review. British Journal of Healthcare and Medical Research, 6(3), 30–60. https://doi.org/10.14738/jbemi.63.9492
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Original Articles
