AIMC Topic: Retina
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Automated Detection of Epiretinal Membranes in OCT Images Using Deep Learning.
Ophthalmic research
Sep 28, 2022
INTRODUCTION: Development and validation of a deep learning algorithm to automatically identify and locate epiretinal memberane (ERM) regions in OCT images.
Training Deep Learning Models to Work on Multiple Devices by Cross-Domain Learning with No Additional Annotations.
Ophthalmology
Sep 22, 2022
PURPOSE: To create an unsupervised cross-domain segmentation algorithm for segmenting intraretinal fluid and retinal layers on normal and pathologic macular OCT images from different manufacturers and camera devices.
A comparison of deep learning U-Net architectures for posterior segment OCT retinal layer segmentation.
Scientific reports
Sep 1, 2022
Deep learning methods have enabled a fast, accurate and automated approach for retinal layer segmentation in posterior segment OCT images. Due to the success of semantic segmentation methods adopting the U-Net, a wide range of variants and improvemen...
Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning.
IEEE journal of biomedical and health informatics
Aug 11, 2022
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...
Robust Detection Model of Vascular Landmarks for Retinal Image Registration: A Two-Stage Convolutional Neural Network.
BioMed research international
Jul 30, 2022
Registration is useful for image processing in computer vision. It can be applied to retinal images and provide support for ophthalmologists in tracking disease progression and monitoring therapeutic responses. This study proposed a robust detection ...
HCTNet: A Hybrid ConvNet-Transformer Network for Retinal Optical Coherence Tomography Image Classification.
Biosensors
Jul 20, 2022
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...
Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions.
Current opinion in ophthalmology
Jul 19, 2022
PURPOSE OF REVIEW: Retinal microvasculature assessment has shown promise to enhance cardiovascular disease (CVD) risk stratification. Integrating artificial intelligence into retinal microvasculature analysis may increase the screening capacity of CV...
Single-shot retinal image enhancement using untrained and pretrained neural networks priors integrated with analytical image priors.
Computers in biology and medicine
Jul 14, 2022
Retinal images acquired using fundus cameras are often visually blurred due to imperfect imaging conditions, refractive medium turbidity, and motion blur. In addition, ocular diseases such as the presence of cataracts also result in blurred retinal i...
Automated image curation in diabetic retinopathy screening using deep learning.
Scientific reports
Jul 1, 2022
Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require curation, a laborious task to perform manually. We developed and validated single and multi-output ...
Multi-class retinal fluid joint segmentation based on cascaded convolutional neural networks.
Physics in medicine and biology
Jun 15, 2022
. Retinal fluid mainly includes intra-retinal fluid (IRF), sub-retinal fluid (SRF) and pigment epithelial detachment (PED), whose accurate segmentation in optical coherence tomography (OCT) image is of great importance to the diagnosis and treatment ...
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