AIMC Topic: Diagnostic Techniques, Ophthalmological
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Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks.
Current eye research
Apr 6, 2021
: This study developed and evaluated a deep learning ensemble method to automatically grade the stages of glaucoma depending on its severity.: After cross-validation of three glaucoma specialists, the final dataset comprised of 3,460 fundus photograp...
Robust Content-Adaptive Global Registration for Multimodal Retinal Images Using Weakly Supervised Deep-Learning Framework.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Feb 25, 2021
Multimodal retinal imaging plays an important role in ophthalmology. We propose a content-adaptive multimodal retinal image registration method in this paper that focuses on the globally coarse alignment and includes three weakly supervised neural ne...
Ophthalmology and the emergence of artificial intelligence.
The Medical journal of Australia
Feb 14, 2021
Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network.
BMC medical imaging
Jan 28, 2021
BACKGROUND: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although...
Choroid Segmentation of Retinal OCT Images Based on CNN Classifier and - Fitter.
Computational and mathematical methods in medicine
Jan 15, 2021
Optical coherence tomography (OCT) is a noninvasive cross-sectional imaging technology used to examine the retinal structure and pathology of the eye. Evaluating the thickness of the choroid using OCT images is of great interests for clinicians and r...
Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach.
Scientific reports
Jan 13, 2021
The purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device's built-in softwa...
Female
Aged
Adult
Diabetic Retinopathy
Neural Networks, Computer
Retrospective Studies
Deep Learning
Tomography, Optical Coherence
Healthy Volunteers
Male
Middle Aged
Case-Control Studies
Humans
Angiography
Fovea Centralis
Image Interpretation, Computer-Assisted
Diagnostic Techniques, Ophthalmological
FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy.
BioMed research international
Jan 2, 2021
Diabetic retinopathy is one of the main causes of blindness in human eyes, and lesion segmentation is an important basic work for the diagnosis of diabetic retinopathy. Due to the small lesion areas scattered in fundus images, it is laborious to segm...
A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs.
Scientific reports
Dec 9, 2020
Fluorescein angiography (FA) is a procedure used to image the vascular structure of the retina and requires the insertion of an exogenous dye with potential adverse side effects. Currently, there is only one alternative non-invasive system based on O...
Applications of deep learning in detection of glaucoma: A systematic review.
European journal of ophthalmology
Dec 4, 2020
Glaucoma is the leading cause of irreversible blindness and disability worldwide. Nevertheless, the majority of patients do not know they have the disease and detection of glaucoma progression using standard technology remains a challenge in clinical...
Machine Learning Techniques for Ophthalmic Data Processing: A Review.
IEEE journal of biomedical and health informatics
Dec 4, 2020
Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approaches proposed for diagnosing ophthalmic diseases during the last four years. Three diseases are addre...
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