Intra-and inter-slice contrastive learning for point supervised oct fluid segmentation

X He, L Fang, M Tan, X Chen - IEEE Transactions on Image …, 2022 - ieeexplore.ieee.org
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 …

Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning

M Treder, JL Lauermann, N Eter - Graefe's Archive for Clinical and …, 2018 - Springer
Purpose Our purpose was to use deep learning for the automated detection of age-related
macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT) …

[HTML][HTML] Detection of morphologic patterns of diabetic macular edema using a deep learning approach based on optical coherence tomography images

Q Wu, B Zhang, Y Hu, B Liu, D Cao, D Yang, Q Peng… - Retina, 2021 - journals.lww.com
Purpose: To develop a deep learning (DL) model to detect morphologic patterns of diabetic
macular edema (DME) based on optical coherence tomography (OCT) images. Methods: In …

RAG-FW: A hybrid convolutional framework for the automated extraction of retinal lesions and lesion-influenced grading of human retinal pathology

T Hassan, MU Akram, N Werghi… - IEEE journal of …, 2020 - ieeexplore.ieee.org
The identification of retinal lesions plays a vital role in accurately classifying and grading
retinopathy. Many researchers have presented studies on optical coherence tomography …

Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data

X Xu, K Lee, L Zhang, M Sonka… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
Automated three-dimensional retinal fluid (named symptomatic exudate-associated
derangements, SEAD) segmentation in 3D OCT volumes is of high interest in the improved …

Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context

A Montuoro, SM Waldstein, BS Gerendas… - Biomedical optics …, 2017 - opg.optica.org
Modern optical coherence tomography (OCT) devices used in ophthalmology acquire
steadily increasing amounts of imaging data. Thus, reliable automated quantitative analysis …

Optical coherence tomography image classification using hybrid deep learning and ant colony optimization

A Khan, K Pin, A Aziz, JW Han, Y Nam - Sensors, 2023 - mdpi.com
Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases.
However, OCT-image-based manual detection by ophthalmologists is prone to errors and …

Multimodal retinal image analysis via deep learning for the diagnosis of intermediate dry age‐related macular degeneration: a feasibility study

E Vaghefi, S Hill, HM Kersten… - Journal of …, 2020 - Wiley Online Library
Background and Objective. To determine if using a multi‐input deep learning approach in
the image analysis of optical coherence tomography (OCT), OCT angiography (OCT‐A), and …

Fluid as a critical biomarker in neovascular age-related macular degeneration management: literature review and consensus recommendations

L Kodjikian, M Parravano, A Clemens, R Dolz-Marco… - Eye, 2021 - nature.com
Current guidelines on the management of patients with neovascular age-related macular
degeneration (nAMD) lack clear recommendations on the interpretation of fluid as seen on …

[HTML][HTML] Prospective, longitudinal study: daily self-imaging with home OCT for neovascular age-related macular degeneration

Y Liu, NM Holekamp, JS Heier - Ophthalmology Retina, 2022 - Elsevier
Objective To validate the performance of the Notal Vision Home OCT (NVHO) system for
daily self-imaging at home and characterize the retinal fluid dynamics of patients with …