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Tomography, Optical Coherence

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Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning.

IEEE journal of biomedical and health informatics
The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely composed of cones responsible for sharp, central vision. The localization of this anatomical landmark in optical coherence tomography (OCT) volumes i...

Predicting demographic characteristics from anterior segment OCT images with deep learning: A study protocol.

PloS one
INTRODUCTION: Anterior segment optical coherence tomography (AS-OCT) is a non-contact, rapid, and high-resolution in vivo modality for imaging of the eyeball's anterior segment structures. Because progressive anterior segment deformation is a hallmar...

Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep Learning.

Ophthalmology. Retina
PURPOSE: To identify disease activity and effects of intravitreal pegcetacoplan treatment on the topographic progression of geographic atrophy (GA) secondary to age-related macular degeneration quantified in spectral-domain OCT (SD-OCT) by automated ...

Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives.

Progress in retinal and eye research
When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount...

Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework.

Computational and mathematical methods in medicine
OBJECTIVE: As an extension of optical coherence tomography (OCT), optical coherence tomographic angiography (OCTA) provides information on the blood flow status at the microlevel and is sensitive to changes in the fundus vessels. However, due to the ...

Cynomolgus monkey's choroid reference database derived from hybrid deep learning optical coherence tomography segmentation.

Scientific reports
Cynomolgus monkeys exhibit human-like features, such as a fovea, so they are often used in non-clinical research. Nevertheless, little is known about the natural variation of the choroidal thickness in relation to origin and sex. A combination of dee...

Correlation of vascular and fluid-related parameters in neovascular age-related macular degeneration using deep learning.

Acta ophthalmologica
PURPOSE: To identify correlations between the vascular characteristics of macular neovascularization (MNV) obtained by optical coherence tomography angiography (OCTA) and distinct retinal fluid volumes in neovascular age-related macular degeneration ...

Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images.

The British journal of ophthalmology
BACKGROUND: The efficiency of clinical trials for retinitis pigmentosa (RP) treatment is limited by the screening burden and lack of reliable surrogate markers for functional end points. Automated methods to determine visual acuity (VA) may help addr...

Deep learning to distinguish Best vitelliform macular dystrophy (BVMD) from adult-onset vitelliform macular degeneration (AVMD).

Scientific reports
Initial stages of Best vitelliform macular dystrophy (BVMD) and adult vitelliform macular dystrophy (AVMD) harbor similar blue autofluorescence (BAF) and optical coherence tomography (OCT) features. Nevertheless, BVMD is characterized by a worse fina...

HCTNet: A Hybrid ConvNet-Transformer Network for Retinal Optical Coherence Tomography Image Classification.

Biosensors
Automatic and accurate optical coherence tomography (OCT) image classification is of great significance to computer-assisted diagnosis of retinal disease. In this study, we propose a hybrid ConvNet-Transformer network (HCTNet) and verify the feasibil...