Retinal Imaging and Image Analysis

By Michael David Abràmoff, Mona Kathryn Garvin, Milan Sonka. IEEE Reviews in Biomedical Engineering, 2010

1,416 citations recorded by the source corpus.

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.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d16abf2c29258519d7b5ad4db9987718e0c94d3b

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Citations in the corpus

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, background): “Some of the review articles ( Abràmoff et al., 2010; Jordan et al., 2017 ) provide a brief introduction to quantitative methods for the analysis of fundus images with a focus on identiﬁcation of retinal lesions and automated techniques for large scale screening for retinal diseases.”
Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs (2018, background): “As opposed to traditional machine learning approaches that rely on features explicitly defined by experts using their domain knowledge 20–23 , CNNs are a type network that can learn features to maximize the network’s ability to distinguish between healthy and disease images.”
Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus (2016, methodology): “These algorithms have been validated extensively (52).”
Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research (2018, background): “Early diagnosis and treatment of DR can prevent vision loss [8].”
Trainable COSFIRE filters for vessel delineation with application to retinal images (2015, background): “…routinely used for the diagnosis of various pathologies, including age-related macular degeneration and diabetic retinopathy – the two leading causes of blindness among people of the Western World (Abramoff et al., 2010) – as well as glaucoma, hypertension, arteriosclerosis and multiple sclerosis.”
Applications of Deep Learning in Fundus Images: A Review (2021, background): “For instance, Abramoff et al. (2010) and Zhang et al. (2014) only focus on classical machine learning methods; Salamat et al.”
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs (2019, neutral): “Color fundus photography (CFP, Figure 1) is currently the most economical, non-invasive imaging modality for inspecting the retina (Abràmoff et al., 2010; Schmidt-Erfurth et al., 2018).”
Artificial intelligence in retina. (2018, background): “In ophthalmology, automated screening based on retinal images has been a target for AI for some time (Abramoff et al., 2013; Abramoff et al., 2010).”
A Review on Medical Image Registration as an Optimization Problem (2017, background): “cn retinal imaging which achieve a comprehensive description of retinal morphology [9].”
Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores (2016, background): “RETINAL fundus images contain rich information about eye-related diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma and systemic diseases, such as hypertension and arteriosclerosis [1], and are thus significant for early computer-aided diagnosis and clinica”
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images (2017, methodology): “The development of automatic tools for the early detection of retinal diseases is valuable since they can be easily integrated in screening programs, where large numbers of images are taken from patient populations, and careful evaluation by physicians is not feasible in a reasonable time [3].”
Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images (2016, background): “As hemorrhages and micro-aneurysms are similar in characteristics and are only differentiable by their size and color on color fundus images, they can be easily confused [16].”