Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network

Antal, 2013, Improving microaneurysm detection in color fundus images by using context-aware approaches, Comput. Med. Imaging Graphics, 37, 403, 10.1016/j.compmedimag.2013.05.001 Bae, 2010, A study on hemorrhage detection using hybrid method in fundus images, J. Digit Imaging, 24, 394, 10.1007/s10278-010-9274-9 Bhaskaranand, 2016, Automated diabetic retinopathy screening and monitoring using retinal fundus image analysis, J. Diabet. Sci. Technol., 10, 254, 10.1177/1932296816628546 Cheung, 2010, Diabetic retinopathy, The Lancet, 376, 124, 10.1016/S0140-6736(09)62124-3 E. Decencière, X. Zhang, G. Cazuguel, B. Laÿ, Feedback on a publicly distributed image database: the Messidor database, Image Analysis & ….(2014). Fleming, 2006, Automated microaneurysm detection using local contrast normalization and local vessel detection, IEEE Trans. Med. Imaging, 25, 1223, 10.1109/TMI.2006.879953 García, 2010, Assessment of four neural network based classifiers to automatically detect red lesions in retinal images, Med. Eng. Phys., 32, 1085, 10.1016/j.medengphy.2010.07.014 Gardner, 1996, Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool, Br. J. Ophthalmol, 80, 940, 10.1136/bjo.80.11.940 Gulshan, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA., 316, 2402, 10.1001/jama.2016.17216 K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: surpassing human-level performance on ImageNet classification, (2015) 1026–1034. Hunt, 2005 Kande, 2009, Automatic detection of microaneurysms and hemorrhages in digital fundus images, J. Digit Imaging, 23, 430, 10.1007/s10278-009-9246-0 Kauppi, 2007, DIARETDB1 diabetic retinopathy database and evaluation protocol Larsen, 2003, Automated detection of fundus photographic red lesions in diabetic retinopathy, Invest. Ophthalmol. Visual Sci, 44, 761, 10.1167/iovs.02-0418 LeCun, 2015, Deep learning, Nature., 521, 436, 10.1038/nature14539 Lee, 2015, Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss, Eye Vision, 1 Leicht, 2014, Microaneurysm turnover in diabetic retinopathy assessed by automated RetmarkerDR image analysis - potential role as biomarker of response to Ranibizumab treatment, Ophthalmologica, 231, 198, 10.1159/000357505 Li, 2015, A cross-modality learning approach for vessel segmentation in retinal images, IEEE Trans. Med. Imaging, 35, 109, 10.1109/TMI.2015.2457891 Liskowski, 2016, Segmenting retinal blood vessels with deep neural networks, IEEE Trans. Med. Imaging, 35, 2369, 10.1109/TMI.2016.2546227 Niemeijer, 2005, Automatic detection of red lesions in digital color fundus photographs, IEEE Trans. Med. Imaging, 24, 584, 10.1109/TMI.2005.843738 Osareh, 2003, Automated identification of diabetic retinal exudates in digital colour images, Br. J. Ophthalmol, 87, 1220, 10.1136/bjo.87.10.1220 Phillips, 1993, Automated detection and quantification of retinal exudates, Graefes Arch. Clin. Exp. Ophthalmol, 231, 90, 10.1007/BF00920219 Quellec, 2008, Optimal wavelet transform for the detection of microaneurysms in retina photographs, IEEE Trans. Med. Imaging, 27, 1230, 10.1109/TMI.2008.920619 Rasmussen, 2014, Microaneurysm count as a predictor of long-term progression in diabetic retinopathy in young patients with type 1 diabetes: the Danish Cohort of Pediatric Diabetes 1987 (DCPD1987), Graefes Arch. Clin. Exp. Ophthalmol, 253, 199, 10.1007/s00417-014-2682-7 Silver, 2016, Mastering the game of Go with deep neural networks and tree search, Nature, 529, 484, 10.1038/nature16961 Sinthanayothin, 2002, Automated detection of diabetic retinopathy on digital fundus images, Diabetes Med., 19, 105, 10.1046/j.1464-5491.2002.00613.x Sopharak, 2009, Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy C-means clustering, Sensors, 9, 2148, 10.3390/s90302148 Sopharak, 2008, Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods, Comput. Med. Imaging Graphics, 32, 720, 10.1016/j.compmedimag.2008.08.009 Tan, 2017, Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network, J. Comput. Sci, 20, 70, 10.1016/j.jocs.2017.02.006 Tang, 2013, Splat feature classification with application to retinal hemorrhage detection in fundus images, IEEE Trans. Med. Imaging, 32, 364, 10.1109/TMI.2012.2227119 van Grinsven, 2016, Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images, IEEE Trans. Med. Imaging, 35, 1273, 10.1109/TMI.2016.2526689 Walter, 2002, A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina, IEEE Trans. Med. Imaging, 21, 1236, 10.1109/TMI.2002.806290 Walter, 2007, Automatic detection of microaneurysms in color fundus images, Med. Image Anal., 11, 555, 10.1016/j.media.2007.05.001 L. Wan, M. Zeiler, S. Zhang, Y.L. Cun, R. Fergus, Regularization of neural networks using DropConnect, (2013) 1058–1066. Wang, 2014, Hierarchical retinal blood vessel segmentation based on feature and ensemble learning, Neurocomputing, 149, 1, 10.1016/j.neucom.2014.07.026 Welfer, 2010, A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images, Comput. Med. Imaging Graphics, 34, 228, 10.1016/j.compmedimag.2009.10.001 Yau, 2012, Global prevalence and major risk factors of diabetic retinopathy, Diabetes Care, 35, 556, 10.2337/dc11-1909