Automated segmentation of optic disc and optic cup for glaucoma assessment using improved UNET++ architecture

Weinreb, 2014, The pathophysiology and treatment of glaucoma: a review, JAMA, 311, 1901, 10.1001/jama.2014.3192 Tham, 2014, Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis, Ophthalmology, 121, 2081, 10.1016/j.ophtha.2014.05.013 Wisaeng, 2014, Automatic detection of optic disc in digital retinal images, Int J Comput Appl, 90, 15 Noronha, 2014, Automated classification of glaucoma stages using higher order cumulant features, Biomed Signal Process Control, 10, 174, 10.1016/j.bspc.2013.11.006 Acharya, 2015, Decision support system for the glaucoma using Gabor transformation, Biomed Signal Process Control, 15, 18, 10.1016/j.bspc.2014.09.004 Haleem, 2016, Regional image features model for automatic classification between normal and glaucoma in fundus and scanning laser ophthalmoscopy (SLO) images, J Med Systems, 40 Diaz-Pinto, A., Morales, S., Naranjo, V., Köhler, T., Mossi, J. and Navea, A., CNNs for automatic glaucoma assessment using fundus images: an extensive validation. BioMed Eng OnLine 18, 2019. Carneiro, Gustavo, Jacinto Nascimento, Andrew P. Bradley. Unregistered multiview mammogram analysis with pre-trained deep learning models, International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, pp. 652-660, 2015. Szegedy, 2015, Going deeper with convolutions, 1 Szegedy, 2016, Rethinking the inception architecture for computer vision, 2818 He, 2016, Deep residual learning for image recognition, 770 Chollet, 2017, Xception: Deep learning with depthwise separable convolutions, 1800 Raghavendra, 2018, Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images, Inf Sci, 441, 41, 10.1016/j.ins.2018.01.051 Yuki Hagiwara, Joel En Wei Koh, Jen Hong Tan, Sulatha V. Bhandary, Augustinus Laude, Edward J. Ciaccio, Louis Tong, U. Rajendra Acharya, Computer-aided diagnosis of glaucoma using fundus images: A review, Computer Methods Programs Biomed, 165, pp. 1-12, 2018. Soltani, 2018, A new expert system based on fuzzy logic and image processing algorithms for early glaucoma diagnosis, Biomed Signal Process Control, 40, 366, 10.1016/j.bspc.2017.10.009 Soorya, 2018, An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection, Int J Med Inf, 110, 52, 10.1016/j.ijmedinf.2017.11.015 Nergiz, 2018, Automated fuzzy optic disc detection algorithm using branching of vessels and color properties in fundus images, Biocybernetics Biomed Eng, 38, 10.1016/j.bbe.2018.08.003 Haleem, 2018, A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis, J Med Syst, 42 Kausu, 2018, Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images, Biocybernetics Biomed Eng, 38, 329, 10.1016/j.bbe.2018.02.003 Mitra, 2018, The region of interest localization for glaucoma analysis from retinal fundus image using deep learning”, Comput Methods Programs Biomed, 165, 25, 10.1016/j.cmpb.2018.08.003 Raghavendra, 2018, Novel expert system for glaucoma identification using non-parametric spatial envelope energy spectrum with fundus images, Biocyber Biomed Eng, 38, 170, 10.1016/j.bbe.2017.11.002 Gómez-Valverde, 2019, Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning, Biomed Opt Express, 10, 892, 10.1364/BOE.10.000892 Guangzhou An, Kazuko Omodaka, Kazuki Hashimoto, Satoru Tsuda, Yukihiro Shiga, Naoko Takada, et al., Glaucoma diagnosis with machine learning based on optical coherence tomography and color fundus images, J Healthcare Eng, vol. 2019, Article ID 4061313, 9 pages, 2019. Lee, 2020, Diagnosing glaucoma with spectral-domain optical coherence tomography using deep learning classifier, J Glaucoma, 29, 287, 10.1097/IJG.0000000000001458 Raja, 2020, Clinically verified hybrid deep learning system for retinal ganglion cells aware grading of glaucomatous progression, IEEE Trans Biomed Eng Hina Raja, M. Usman Akram, Sajid Gul Khawaja, Muhammad Arslan, Aneeqa Ramzan, Noman Nazir, Data on OCT and fundus images for the detection of glaucoma, Data in Brief, vol. 29, 2020. Asaoka, 2019, Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images, Am J Ophthalmol, 198, 136, 10.1016/j.ajo.2018.10.007 Alice C. Verticchio Vercellin, Firas Jassim, Linda Yi-Chieh Poon, Edem Tsikata, Boy Braaf, Sneha Shah, et al., Diagnostic capability of three-dimensional macular parameters for glaucoma using optical coherence tomography volume scans, Invest Ophthalmol Vis Sci., 2018. Greg Russell, Silvia N. W. Hertzberg, Natalia Anisimova, Natalia Gavrilova, Beáta É. Petrovski, Goran Petrovski, Digital image analysis of the angle and optic nerve: a simple, fast, and low-cost method for glaucoma assessment, J Ophthalmol, 2020, Article ID 3595610, 8 pages, 2020. Muramatsu C., Diagnosis of glaucoma on retinal fundus images using deep learning: detection of nerve fiber layer defect and optic disc analysis, Deep Learning in Medical Image Analysis. Advances in Experimental Medicine and Biology, vol. 1213, Springer, 2020. Sevastopolsky, 2017, Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network, Pattern Recognit Image Anal, 27, 618, 10.1134/S1054661817030269 Ronneberger O, Fischer P, Brox T, U-net: Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, pp 234–241. Springer, 2015. Fu, 2018, Disc-aware ensemble network for glaucoma screening from fundus image, IEEE Trans Med Imag, 37, 2493, 10.1109/TMI.2018.2837012 Bajwa MN, Malik MI, Siddiqui SA, Dengel A, Shafait F, Neumeier W, Ahmed S., Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Med Inform Decis Mak, 19(136), 2019. Fu, 2018, Joint optic disc and cup segmentation based on multi-label deep network and polar transformation, IEEE Trans Med Imaging, 37, 1597, 10.1109/TMI.2018.2791488 Orlando, 2020, REFUGE challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs, Med Image Anal, 59, 10.1016/j.media.2019.101570 Li, 2020, Deep learning-based automated Detection of glaucomatous optic neuropathy on color fundus photographs, Graefe's Arch Clin Exp Ophthalmol, 258, 851, 10.1007/s00417-020-04609-8 Al Ghamdi, 2019, Semi-supervised transfer learning for convolutional neural networks for glaucoma detection, 3812 Pathan, 2020, Automated detection of optic disc contours in fundus images using decision tree classifier, Biocyber Biomed Eng, 40, 52, 10.1016/j.bbe.2019.11.003 Jiang, 2019, Optic disc and cup segmentation based on deep convolutional generative adversarial networks, IEEE Access, 7, 64483, 10.1109/ACCESS.2019.2917508 Gheisari S, Shariflou S, Phu J. et al., A combined convolutional and recurrent neural network for enhanced glaucoma detection. Scientific Rep, vol. 11, 2021. Bisneto, 2020, Generative adversarial network and texture features applied to automatic glaucoma detection, Appl Soft Comput, 90, 10.1016/j.asoc.2020.106165 Tekouabou Koumetio, 2021, Using deep features extraction and ensemble classifiers to detect glaucoma from fundus images Deng, 2009, Imagenet: a large-scale hierarchical image database, 248 Gour, 2020, Automated glaucoma detection using GIST and pyramid histogram of oriented gradients (PHOG) descriptors, Pattern Recogn Lett, 137, 3, 10.1016/j.patrec.2019.04.004 Wang, 2019, Boundary and entropy-driven adversarial learning for fundus image segmentation, 102 Li, 2020, A large-scale database and a CNN model for attention-based glaucoma detection, IEEE Trans Med Imaging, 39, 413, 10.1109/TMI.2019.2927226 Shah, 2019, Dynamic region proposal networks for semantic segmentation in automated glaucoma screening, 578 Pruthi, 2020, Optic Cup segmentation from retinal fundus images using Glowworm Swarm Optimization for glaucoma detection, Biomed Signal Process Control, 60, 102004, 10.1016/j.bspc.2020.102004 Sumaiya Pathan, Preetham Kumar, Radhika M. Pai, Sulatha V. Bhandary, Automated segmentation and classifcation of retinal features for glaucoma diagnosis, Biomed Signal Process Control, 63, 2021. Gao Y, Yu X, Wu C, Zhou W, Wang X, Zhuang Y. Accurate optic disc and cup segmentation from retinal images using a multi-feature based approach for glaucoma assessment, Symmetry., 11(10), 2019. Wang, 2021, Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network, Pattern Recogn, 112, 10.1016/j.patcog.2020.107810 Jiang Y, Wang F, Gao J, Cao S. Multi-path recurrent U-Net segmentation of retinal fundus image. Appl. Sci.., 10(11), 2020. Jin, 2020, Optic disc segmentation using attention-based U-Net and the improved cross-entropy convolutional neural network, Entropy, 22, 10.3390/e22080844 Decencière, 2014, Feedback on a publicly distributed image database: The Messidor database, Image Anal Stereol, 33, 231, 10.5566/ias.1155 Singh, 2021, An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus, Med Biol Eng Compu, 59, 333, 10.1007/s11517-020-02307-5 Shruti Jadon, A survey of loss functions for semantic Segmentation, IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1-7, 2020. Zhou, 2019, Unet++: Redesigning skip connections to exploit multiscale features in image segmentation, IEEE Trans Med Imaging, 39, 1856, 10.1109/TMI.2019.2959609 J. Sivaswamy S.R. Krishnadas G. Datt Joshi M. Jain A.U. Syed Tabish Drishti-gs: Retinal image dataset for optic nerve head (onh) segmentation IEEE 11th International Symposium on Biomedical Imaging (ISBI). IEEE 2014 53 56 Zhang Z, Yin FS, Liu J, Wong WK, Tan NM, Lee BH, Cheng J, Wong TY., Origa-light: An online retinal fundus image database for glaucoma analysis and research., IEEE Engineering in Medicine and Biology Society. Annual International Conference, pp. 3065–3068, 2010. Fumero, Francisco et al, Interactive tool and database for optic disc and cup segmentation of stereo and monocular retinal fundus images, 23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, IEEE, 2015. Carmona, 2008, Identification of the optic nerve head with genetic algorithms, Artificial Intel Med, Elsevier, 43, 243, 10.1016/j.artmed.2008.04.005 Prenzel, 2006, Spectral and spatial filtering for enhanced thematic change analysis of remotely sensed data, Int J Remote Sens, 27, 835, 10.1080/01431160500300321 Orhan, 2018, Skip connections eliminate singularities Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger, Densely Connected Convolutional Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261-2269, 2017. Lee, 2015, Deeply-supervised nets, Artificial Intel Statistics, 38, 562 Ma Yi-de, Liu Qing, Qian Zhi-bai, Automated image segmentation using improved PCNN model based on cross-entropy, International Symposium on Intelligent Multimedia, Video and Speech Processing, pp. 743–746, IEEE, 2004. Gu, 2019, CE-Net: context encoder network for 2D medical image segmentation, IEEE Trans Med Imaging, 38, 2281, 10.1109/TMI.2019.2903562 Harizman, 2006, The ISNT rule and differentiation of normal from glaucomatous eyes, Arch Ophthalmol, 124, 1579, 10.1001/archopht.124.11.1579 Chaumette, 2004, Image moments: a general and useful set of features for visual servoing, IEEE Trans Rob, 20, 713, 10.1109/TRO.2004.829463 Zilly J.G, Buhmann JM, Mahapatra D, Boosting convolutional filters with entropy sampling for optic cup and disc image segmentation from fundus images., International Workshop on Machine Learning in Medical Imaging, pp. 136–143, Springer, 2015. Zilly, 2017, Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation, Comput Med Imaging Graph, 55, 28, 10.1016/j.compmedimag.2016.07.012 Juneja, 2020, Automated detection of glaucoma using deep learning convolution network (G-net), Multimedia Tools Applications, 79, 15531, 10.1007/s11042-019-7460-4 Edupuganti V.G., Chawla A., Kale A., Automatic optic disk and cup segmentation of fundus images using deep learning, IEEE International Conference on Image Processing, IEEE, pp. 2227-2231, 2018. Zhou W, Wu C, Gao Y, Yu X, Automatic optic disc boundary extraction based on saliency object detection and modified local intensity clustering model in retinal images, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E100.A, no. 9, pp. 2069–2072, 2017. Cheng, 2013, Superpixel classification based optic disc and optic cup segmentation for glaucoma screening, IEEE Trans Med Imaging, 32, 1019, 10.1109/TMI.2013.2247770 Mittapalli, 2016, Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma, Biomed Signal Process Control, 24, 34, 10.1016/j.bspc.2015.09.003 Wei Zhou, Yugen Yi, Yuan Gao, Jiangyan Dai, Optic disc and cup segmentation in retinal images for glaucoma diagnosis by locally statistical active contour model with structure prior, Computational and Mathematical Methods in Medicine, vol. 2019, Article ID 8973287, 16 pages, 2019. Shankaranarayana, 2019, Fully convolutional networks for monocular retinal depth estimation and optic disc-cup segmentation, IEEE J Biomed Health Informatics, 23, 1417, 10.1109/JBHI.2019.2899403 Maninis, Kevis-Kokitsi, Pont-Tuset, Jordi, Arbelaez, Pablo, Van Gool, Luc, Deep Retinal Image Understanding, Medical Image Computing and Computer-Assisted Intervention – MICCAI, 2016. Koh, 2017, Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies, Comput Biol Med, 84, 89, 10.1016/j.compbiomed.2017.03.008 Nugroho, 2015, Detection of exudates on color fundus images using texture-based feature extraction, Int J Technol, 6, 121, 10.14716/ijtech.v6i2.958 Guo, 2020, Automated glaucoma screening method based on image segmentation and feature extraction, Med Biol Eng Compu, 58, 2567, 10.1007/s11517-020-02237-2 Fatima Bokhari, 2017, Fundus image segmentation and feature extraction for the detection of glaucoma: A new approach, Curr Med Imaging, 14, 77, 10.2174/1573405613666170405145913 Perdomo, 2018, Glaucoma diagnosis from eye fundus images based on deep morphometric feature estimation, 319 Lotankar, 2015, Detection of optic disc and cup from color retinal images for automated diagnosis of glaucoma, 1 Issac, 2015, An adaptive threshold based image processing technique for improved glaucoma detection and classification, Comput Methods Programs Biomed, 122, 229, 10.1016/j.cmpb.2015.08.002 Al Ghamdi M, Semi-supervised transfer learning for convolutional neural networks for glaucoma detection IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3812-3816, 2019. Sarkar D, Das S et al, “Automated glaucoma detection of medical image using biogeography based optimization.” Advances in Optical Science and Engineering. Springer Proceedings in Physics, vol. 194, pp. 381–388, 2017. Abbas, 2017, Glaucoma-deep: detection of glaucoma eye disease on retinal fundus images using deep learning, Int J Adv Comput Sci Appl, 8, 41