Evaluation of Age-related Macular Degeneration With Optical Coherence Tomography

Keane, Pearse A.; Patel, Praveen J; Liakopoulos, Sandra; Heussen, Florian M.; Sadda, Srinivas R.; Tufail, Adnan

doi:10.1016/j.survophthal.2012.01.006

Cited by 240 publications

(200 citation statements)

References 252 publications

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“…32 Evaluation of L-CNV in vivo using OCT imaging has been previously studied in different species such as monkeys, 33,34 rabbits, 35 and rats. 27,36,37 Others have evaluated OCT in the mouse model of L-CNV used here by comparing OCT imaging to immunostaining and histological analyses.…”

Section: Discussionmentioning

confidence: 99%

A Simple Optical Coherence Tomography Quantification Method for Choroidal Neovascularization

Sulaiman

Quigley

et al. 2015

Journal of Ocular Pharmacology and Therapeutics

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Purpose: Therapeutic efficacy is routinely assessed by measurement of lesion size using flatmounted choroids and confocal microscopy in the laser-induced choroidal neovascularization (L-CNV) rodent model. We investigated whether optical coherence tomography (OCT) quantification, using an ellipsoid volume measurement, was comparable to standard ex vivo evaluation methods for this model and whether this approach could be used to monitor treatment-related lesion changes. Methods: Bruch's membrane was ruptured by argon laser in the dilated eyes of C57BL/6J mice, followed by intravitreal injections of anti-VEGF 164 or vehicle, or no injection. In vivo OCT images were acquired using Micron III or InVivoVue systems at 7, 10, and/or 14 days post-laser and neovascular lesion volume was calculated as an ellipsoid. Subsequently, lesion volume was compared to that calculated from confocal Z-stack images of agglutinin-stained choroidal flatmounts. Results: Ellipsoid volume measurement of orthogonal 2-dimensional OCT images obtained from different imaging systems correlated with ex vivo lesion volumes for L-CNV (Spearman's r = 0.82, 0.75, and 0.82 at days 7, 10, and 14, respectively). Ellipsoid volume calculation allowed temporal monitoring and evaluation of CNV lesions in response to antivascular endothelial growth factor treatment. Conclusions: Ellipsoid volume measurements allow rapid, quantitative use of OCT for the assessment of CNV lesions in vivo. This novel method can be used with different OCT imaging systems with sensitivity to distinguish between treatment conditions. It may serve as a useful adjunct to the standard ex vivo confocal quantification, to assess therapeutic efficacy in preclinical models of CNV, and in models of other ocular diseases.

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Section: Discussionmentioning

confidence: 99%

A Simple Optical Coherence Tomography Quantification Method for Choroidal Neovascularization

Sulaiman

Quigley

et al. 2015

Journal of Ocular Pharmacology and Therapeutics

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“…OCT has been particularly useful in the assessment and characterization of multiple sclerosis (MS) through the analysis of the thickness of various retinal layers [2][3][4]. It has also seen use for exploring numerous other diseases, including age-related macular degeneration [5], diabetes [6], Alzheimer's disease [7], Parkinson's disease [8], and glaucoma [9]. While automated and semi-automated methods for analyzing and segmenting retinal layers in OCT have been introduced regularly over the past decade [10][11][12][13][14][15], there has been very little development of image registration methods for OCT.…”

Section: Introductionmentioning

confidence: 99%

Analysis of macular OCT images using deformable registration

Chen¹,

Lang²,

Ying³

et al. 2014

Biomed. Opt. Express

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Optical coherence tomography (OCT) of the macula has become increasingly important in the investigation of retinal pathology. However, deformable image registration, which is used for aligning subjects for pairwise comparisons, population averaging, and atlas label transfer, has not been well-developed and demonstrated on OCT images. In this paper, we present a deformable image registration approach designed specifically for macular OCT images. The approach begins with an initial translation to align the fovea of each subject, followed by a linear rescaling to align the top and bottom retinal boundaries. Finally, the layers within the retina are aligned by a deformable registration using one-dimensional radial basis functions. The algorithm was validated using manual delineations of retinal layers in OCT images from a cohort consisting of healthy controls and patients diagnosed with multiple sclerosis (MS). We show that the algorithm overcomes the shortcomings of existing generic registration methods, which cannot be readily applied to OCT images. A successful deformable image registration algorithm for macular OCT opens up a variety of population based analysis techniques that are regularly used in other imaging modalities, such as spatial normalization, statistical atlas creation, and voxel based morphometry. Examples of these applications are provided to demonstrate the potential benefits such techniques can have on our understanding of retinal disease. In particular, included is a pilot study of localized volumetric changes between healthy controls and MS patients using the proposed registration algorithm.

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“…The retinal structures may be clearly captured by the OCT's micrometer-level resolution. Some ophthalmological diseases like age-related macular degeneration (AMD) and diabetic macular edema (DME) are diagnosed based on the OCT images [3,4]. The existing guideline diagnoses these retinal diseases based on the visual inspection of the OCT images by the welltrained ophthalmologists.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

Wang

Zhang

Yao

et al. 2016

Biomed. Opt. Express

130

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Non-lethal macular diseases greatly impact patients' life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples.

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Evaluation of Age-related Macular Degeneration With Optical Coherence Tomography

Cited by 240 publications

References 252 publications

A Simple Optical Coherence Tomography Quantification Method for Choroidal Neovascularization

A Simple Optical Coherence Tomography Quantification Method for Choroidal Neovascularization

Analysis of macular OCT images using deformable registration

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

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