Simultaneous multiclass retinal lesion segmentation using fully automated RILBP-YNet in diabetic retinopathy
Tài liệu tham khảo
Monemian, 2023, Detecting red - lesions from retinal fundus images using unique morphological features, Sci. Rep., 1
Guo, 2019, L-Seg: An end-to-end unified framework for multi-lesion segmentation of fundus images, Neurocomputing., 349, 52, 10.1016/j.neucom.2019.04.019
Salz, 2015, Imaging in diabetic retinopathy, Middle East Afr. J. Ophthalmol., 22, 145, 10.4103/0974-9233.151887
Diabetic retinopathy - Diagnosis and treatment - Mayo Clinic, (2018). https://www.mayoclinic.org/diseases-conditions/diabetic-retinopathy/diagnosis-treatment/drc-20371617.
International Diabetes Federation, International Federation on Ageing, International Agency for the Prevention of Blindness, The Diabetic Retinopathy Barometer Report: Global findings, 2019. https://www.iapb.org/wp-content/uploads/DR-Global-Report-1.pdf.
“A Report by The Minister of State in the Ministry of Health and Family Welfare, India, Shri Ashwini Kumar Choubey” 2019., 2019. http://164.100.24.220/loksabhaquestions/annex/172/AU1915.pdf.
P. Porwal, S.P. Id, R.K. Id, M. Kokare, Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research, (n.d.) 1–8, doi: 10.3390/data3030025.
T. Ojala, M. Pietikainen, D. Harwood, Performance evaluation of texture measures with classification based on Kullback discrimination of distributions, in: Proc. 12th Int. Conf. Pattern Recognit., IEEE Comput. Soc. Press, 2002: pp. 582–585, doi: 10.1109/ICPR.1994.576366.
Porwal, 2020, IDRiD: Diabetic retinopathy – segmentation and grading challenge, Med. Image Anal., 59, 101561, 10.1016/j.media.2019.101561
Karn, 2019, Robust retinal blood vessel segmentation using hybrid active contour model, IET Image Process., 13, 440, 10.1049/iet-ipr.2018.5413
B. Biswal, P. Geetha Pavani, P.K. Biswal, Controlled differential evolution based detection of neovascularization on optic disc using support vector machine (2020) 1–10, doi: 10.1515/bmt-2020-0110.
Biswal, 2018, Robust retinal blood vessel segmentation using line detectors with multiple masks, IET Image Process., 12, 389, 10.1049/iet-ipr.2017.0329
Chandrakumar, 2016, Classifying diabetic retinopathy using deep learning architecture, Int. J. Eng. Res. V, 5, 19
Pratt, 2016, Convolutional neural networks for diabetic retinopathy, Proc. Comput. Sci., 90, 200, 10.1016/j.procs.2016.07.014
Wu, 2017, Automatic detection of microaneurysms in retinal fundus images, Comput. Med. Imaging Graph., 55, 106, 10.1016/j.compmedimag.2016.08.001
Ege, 2000, Screening for diabetic retinopathy using computer based image analysis and statistical classification, Comput. Methods Programs Biomed., 62, 165, 10.1016/S0169-2607(00)00065-1
Roychowdhury, 2014, DREAM: Diabetic retinopathy analysis using machine learning, IEEE J. Biomed. Heal. Informatics., 18, 1717, 10.1109/JBHI.2013.2294635
Jebaseeli, 2019, Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVM, Optik (Stuttg), 199
Welikala, 2014, Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification, Comput. Methods Programs Biomed., 114, 247, 10.1016/j.cmpb.2014.02.010
Sopharak, 2008, Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods, Comput. Med. Imaging Graph., 32, 720, 10.1016/j.compmedimag.2008.08.009
He, 2020, Segmenting diabetic retinopathy lesions in multispectral images using low-dimensional spatial-spectral matrix representation, IEEE J. Biomed. Heal. Informatics., 24, 493, 10.1109/JBHI.2019.2912668
Sopharak, 2009, Automatic exudate detection from non-dilated diabetic retinopathy retinal images using Fuzzy C-means clustering, Sensors., 9, 2148, 10.3390/s90302148
Padmasini, 2016, Detection of neovascularisation using K-means clustering through registration of peripapillary OCT and fundus retinal images, 2016 IEEE Int. Conf. Comput. Intell. Comput. Res. ICCIC, 2017, 1
Y.M. Rajput, Extraction of cotton wool spot using multi resolution analysis and classification using K-means clustering, 2015, 6–10.
Lachure, 2015, Diabetic Retinopathy using morphological operations and machine learning, Souvenir 2015 IEEE Int. Adv. Comput. Conf. IACC, 2015, 617, 10.1109/IADCC.2015.7154781
Lecun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Ronneberger, 2015, U-net: Convolutional networks for biomedical image segmentation, Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)., 9351, 234
Zhou, 2018
Cicek, 2016, 3D U-Net: Learning dense volumetric segmentation from sparse annotation, Med. Image Comput. Comput. Interv., 424
Ding, 2020, CAB U-Net: An end-to-end category attention boosting algorithm for segmentation, Comput. Med. Imaging Graph., 84, 10.1016/j.compmedimag.2020.101764
Hu, 2019, S-UNet: A bridge-style U-net framework with a saliency mechanism for retinal vessel segmentation, IEEE Access., 7, 174167, 10.1109/ACCESS.2019.2940476
Biswal, 2021, Robust segmentation of exudates from retinal surface using M-CapsNet via EM routingBiomed, Signal Process Control., 68, 102770, 10.1016/j.bspc.2021.102770
Wu, 2020, NFN+: A novel network followed network for retinal vessel segmentation, Neural Networks., 126, 153, 10.1016/j.neunet.2020.02.018
Jiang, 2020, Multi-path recurrent U-Net segmentation of retinal fundus image, Appl. Sci., 10, 3777, 10.3390/app10113777
Tan, 2017, Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network, Inf. Sci. (Ny), 420, 66, 10.1016/j.ins.2017.08.050
Zeng, 2019, RIC-Unet: An improved neural network based on UNET for nuclei segmentation in histology images, IEEE Access., 7, 21420, 10.1109/ACCESS.2019.2896920
Badrinarayanan, 2017, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 39, 2481, 10.1109/TPAMI.2016.2644615
Shelhamer, 2017, Fully convolutional networks for semantic segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 39, 640, 10.1109/TPAMI.2016.2572683
Chen, 2018, DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Trans. Pattern Anal. Mach. Intell., 40, 834, 10.1109/TPAMI.2017.2699184
Noh, 2015, Learning deconvolution network for semantic segmentation, Proc. IEEE Int. Conf. Comput. Vis. 2015 Inter, 1520
Wan, 2021, EAD-Net: A novel lesion segmentation method in diabetic retinopathy using neural networks, Dis. Markers., 2021, 1
Kou, 2020, An enhanced residual u-net for microaneurysms and exudates segmentation in fundus images, IEEE Access., 8, 185514, 10.1109/ACCESS.2020.3029117
Guo, 2022, CARNet: Cascade attentive RefineNet for multi-lesion segmentation of diabetic retinopathy images, Complex Intell. Syst., 8, 1681, 10.1007/s40747-021-00630-4
M. Siebert, R. Philipp, Multi-task lesion segmentation with a lightweight U 2 -Net to enhance explainability of mobile screening systems for diabetic retinopathy, 2021.
Garifullin, 2021, Deep Bayesian baseline for segmenting diabetic retinopathy lesions: Advances and challenges, Comput. Biol. Med., 136, 10.1016/j.compbiomed.2021.104725
Decencière, 2013, TeleOphta : Machine learning and image processing methods for teleophthalmology, 34, 196
Staal, 2004, Ridge-based vessel segmentation in color images of the retina, IEEE Trans. Med. Imaging., 23, 501, 10.1109/TMI.2004.825627
M.M.D. Goldbaum, The STARE Project, U.S. Natl. Institutes Heal. (2004). http://www.ces.clemson.edu/∼ahoover/stare.
CHASE_DB1 | Retinal image database | Retinal Image Analysis, (n.d.). https://blogs.kingston.ac.uk/retinal/chasedb1/.
Mo, 2018, Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks, Neurocomputing., 290, 161, 10.1016/j.neucom.2018.02.035
Xie, 2015, Holistically-nested edge detection, Proc. IEEE Int. Conf. Comput. Vis. 2015 Inter, 1395
Z. Yu, C. Feng, M.Y. Liu, S. Ramalingam, CASENet: Deep category-aware semantic edge detection, in: Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, 2017-Janua (2017) 1761–1770, doi: 10.1109/CVPR.2017.191.
L.C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 11211 LNCS, 2018, pp. 833–851, doi: 10.1007/978-3-030-01234-2_49.
Sarhan, 2019, Multi-scale microaneurysms segmentation using embedding triplet loss, 174
Yan, 2019, Learning mutually local-global U-Nets for high-resolution retinal lesion segmentation in fundus images, 2019
Xue, 2019, Knowledge-based systems deep membrane systems for multitask segmentation in diabetic retinopathy, Knowledge-Based Syst., 183, 104887, 10.1016/j.knosys.2019.104887
Libiao, 2021, Semantic segmentation based on DeeplabV3+ with multiple fusions of low-level features, IAEAC 2021 - IEEE 5th Adv Inf. Technol. Electron. Autom. Control Conf., 2021, 1957
S. Mehta, E. Mercan, J. Bartlett, D. Weaver, J.G. Elmore, L. Shapiro, S.M. B, E. Mercan, J. Bartlett, D. Weaver, J.G. Elmore, L. Shapiro, Y-Net: Joint segmentation and classification for diagnosis of breast biopsy images, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 11071 LNCS (2018) pp. 893–901, doi: 10.1007/978-3-030-00934-2_99.
Morales, 2017, Retinal disease screening through local binary patterns, IEEE J. Biomed. Heal. Informat., 21, 184, 10.1109/JBHI.2015.2490798
Yan, 2016, Semantic indexing with deep learning: a case study, Big Data Anal., 1, 1, 10.1186/s41044-016-0007-z