Retinal Imaging and Image Analysis
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TLDR
Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed and aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.Abstract:
Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.read more
Citations
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Journal ArticleDOI
Multi-Scale Segmentation and Surface Fitting for Measuring 3-D Macular Holes
Amar Vijai Nasrulloh,Chris G. Willcocks,Philip T. Jackson,Caspar Geenen,Maged Habib,David H. W. Steel,Boguslaw Obara +6 more
TL;DR: This work introduces a multi-scale 3-D level set segmentation approach based on a state-of-the-art level set method, and introduces novel curvature-based cutting and3-D measurement procedures.
Journal ArticleDOI
Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation
TL;DR: A multi-scale channel importance sorting and important spatial information positioning encoder–decoder for segmentation in Retinal Vessels can greatly decrease false positive rate of the blood vessels at the ends and enhance the sharpness of retinal vessels.
Journal ArticleDOI
Automated 3D Segmentation of Intraretinal Surfaces in SD-OCT Volumes in Normal and Diabetic Mice.
Bhavna J. Antony,Woojin Jeong,Woojin Jeong,Michael D. Abràmoff,Joseph Vance,Elliott H. Sohn,Mona K. Garvin +6 more
TL;DR: The presented method, initially developed for human OCT, has been adapted for mice, with the potential to be adapted for other animals as well and is expected to be useful in the quantitative study of intraretinal layers in mice.
Journal ArticleDOI
Antiangiogenic and Neurogenic Activities of Sleeping Beauty-Mediated PEDF-Transfected RPE Cells In Vitro and In Vivo.
Sandra Johnen,Yassin Djalali-Talab,Olga Kazanskaya,Theresa Möller,Nina Harmening,Martina Kropp,Zsuzsanna Izsvák,Peter Walter,Gabriele Thumann +8 more
TL;DR: It is shown that transplantation of pigment epithelial cells overexpressing PEDF can restore a permissive subretinal environment for RPE and photoreceptor maintenance, while inhibiting choroidal blood vessel growth.
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GC-NET for classification of glaucoma in the retinal fundus image
TL;DR: A deep learning-based glaucoma classification network (GC-NET) for classifying a retinal image as glaucatous or non-glaucomatous and results showed that GC-NET achieves accuracy of 97.51%, sensitivity of 98.78% and specificity of 96.20% which outperforms state of the art.
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