[HTML][HTML] AI-based monitoring of retinal fluid in disease activity and under therapy
U Schmidt-Erfurth, GS Reiter, S Riedl… - Progress in retinal and …, 2022 - Elsevier
Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by
high-resolution three-dimensional optical coherence tomography (OCT), which is used …
high-resolution three-dimensional optical coherence tomography (OCT), which is used …
Deep learning in retinal optical coherence tomography (OCT): A comprehensive survey
Retinal optical coherence tomography (OCT) images provide fundamental information
regarding the health of the posterior eye (eg, the retina and choroid). Thus, the development …
regarding the health of the posterior eye (eg, the retina and choroid). Thus, the development …
[HTML][HTML] Quantitative analysis of OCT for neovascular age-related macular degeneration using deep learning
Purpose To apply a deep learning algorithm for automated, objective, and comprehensive
quantification of optical coherence tomography (OCT) scans to a large real-world dataset of …
quantification of optical coherence tomography (OCT) scans to a large real-world dataset of …
Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy
Background In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate
estimation of multi-class retinal fluid (MRF) is required for the activity prescription and …
estimation of multi-class retinal fluid (MRF) is required for the activity prescription and …
Intra-and inter-slice contrastive learning for point supervised oct fluid segmentation
OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …
Brain MRI analysis using a deep learning based evolutionary approach
H Shahamat, MS Abadeh - Neural Networks, 2020 - Elsevier
Convolutional neural network (CNN) models have recently demonstrated impressive
performance in medical image analysis. However, there is no clear understanding of why …
performance in medical image analysis. However, there is no clear understanding of why …
RetiFluidNet: a self-adaptive and multi-attention deep convolutional network for retinal OCT fluid segmentation
Optical coherence tomography (OCT) helps ophthalmologists assess macular edema,
accumulation of fluids, and lesions at microscopic resolution. Quantification of retinal fluids is …
accumulation of fluids, and lesions at microscopic resolution. Quantification of retinal fluids is …
TSSK-Net: Weakly supervised biomarker localization and segmentation with image-level annotation in retinal OCT images
The localization and segmentation of biomarkers in OCT images are critical steps in retina-
related disease diagnosis. Although fully supervised deep learning models can segment …
related disease diagnosis. Although fully supervised deep learning models can segment …
A deep learning approach to denoise optical coherence tomography images of the optic nerve head
Optical coherence tomography (OCT) has become an established clinical routine for the in
vivo imaging of the optic nerve head (ONH) tissues, that is crucial in the diagnosis and …
vivo imaging of the optic nerve head (ONH) tissues, that is crucial in the diagnosis and …
Methodological challenges of deep learning in optical coherence tomography for retinal diseases: a review
Artificial intelligence (AI)-based automated classification and segmentation of optical
coherence tomography (OCT) features have become increasingly popular. However, its 3 …
coherence tomography (OCT) features have become increasingly popular. However, its 3 …