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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.

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Robust segmentation of vascular network using deeply cascaded AReN-UNet

TL;DR: A novel cascaded AReN-UNet (Attention Residual U Network), driven by the integration of attention and residual modules, is presented, which reduces the vessel breakdowns in the vascular map.
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A computational framework to investigate retinal haemodynamics and tissue stress

TL;DR: A computational framework involving 3D fluid–structure interaction simulations derived from fundus images that work towards creating unique data on retinal biomechanics, and methods to convert 2D fundus photographs into 3D geometries that follow the curvature of the retina.
Proceedings ArticleDOI

Detection and registration of vessels of fundus and OCT images using curevelet analysis

TL;DR: The first step to combine the different modalities is to register color fundus images with OCT projection, and curvelet transform is used to extract vessels for both modalities, and the extracted vessels from two modalities are registered together.
Journal ArticleDOI

Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models

TL;DR: It is concluded that fundus images contain valuable information about the systemic characteristics of a patient and to optimize DL model performance, it is recommended that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy.
References
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Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI

Optical coherence tomography

TL;DR: OCT as discussed by the authors uses low-coherence interferometry to produce a two-dimensional image of optical scattering from internal tissue microstructures in a way analogous to ultrasonic pulse-echo imaging.
Journal ArticleDOI

A taxonomy and evaluation of dense two-frame stereo correspondence algorithms

TL;DR: This paper has designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms.
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