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
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.
Journal ArticleDOI
A computational framework to investigate retinal haemodynamics and tissue stress
Joseph Rebhan,Louis P. Parker,Louis P. Parker,Lachlan J. Kelsey,Lachlan J. Kelsey,Fred K. Chen,Fred K. Chen,Fred K. Chen,Barry J. Doyle +8 more
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
Nergis Khan,Chandrasan Perera,Eliot R. Dow,Karen Mei Chen,Vinit B. Mahajan,Prithvi Mruthyunjaya,Diana V. Do,Theodore Leng,David Myung +8 more
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.
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