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Optic Disk

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3-LbNets: Tri-Labeling Deep Convolutional Neural Network for the Automated Screening of Glaucoma, Glaucoma Suspect, and No Glaucoma in Fundus Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Early detection of glaucoma, a widespread visual disease, can prevent vision loss. Unfortunately, ophthalmologists are scarce and clinical diagnosis requires much time and cost. Therefore, we developed a screening Tri-Labeling deep convolutional neur...

A Deep Learning-Based Approach Towards Simultaneous Localization of Optic Disc and Fovea from Retinal Fundus Images.

Studies in health technology and informatics
In this work, we propose a multi-task learning-based approach towards the localization of optic disc and fovea from human retinal fundus images using a deep learning-based approach. Formulating the task as an image-based regression problem, we propos...

Joint optic disk and cup segmentation for glaucoma screening using a region-based deep learning network.

Eye (London, England)
OBJECTIVES: To develop and validate an end-to-end region-based deep convolutional neural network (R-DCNN) to jointly segment the optic disc (OD) and optic cup (OC) in retinal fundus images for precise cup-to-disc ratio (CDR) measurement and glaucoma ...

A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.

Medical physics
BACKGROUND: Most of existing deep learning research in medical image analysis is focused on networks with stronger performance. These networks have achieved success, while their architectures are complex and even contain massive parameters ranging fr...

Through the eyes into the brain, using artificial intelligence.

Annals of the Academy of Medicine, Singapore
INTRODUCTION: Detection of neurological conditions is of high importance in the current context of increasingly ageing populations. Imaging of the retina and the optic nerve head represents a unique opportunity to detect brain diseases, but requires ...

Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma.

Translational vision science & technology
PURPOSE: (1) To assess the performance of geometric deep learning in diagnosing glaucoma from a single optical coherence tomography (OCT) scan of the optic nerve head and (2) to compare its performance to that obtained with a three-dimensional (3D) c...

Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma.

Current eye research
PURPOSE: We applied deep learning-based noise reduction (NR) to optical coherence tomography-angiography (OCTA) images of the radial peripapillary capillaries (RPCs) in eyes with glaucoma and investigated the usefulness of this method as an objective...

Deep learning approaches based improved light weight U-Net with attention module for optic disc segmentation.

Physical and engineering sciences in medicine
Glaucoma is a major cause of blindness worldwide, and its early detection is essential for the timely management of the condition. Glaucoma-induced anomalies of the optic nerve head may cause variation in the Optic Disc (OD) size. Therefore, robust O...

Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis.

Scientific reports
This work aims at determining the ability of a deep learning (DL) algorithm to measure retinal nerve fiber layer (RNFL) thickness from optical coherence tomography (OCT) scans in anterior ischemic optic neuropathy (NAION) and demyelinating optic neur...

Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma.

JAMA ophthalmology
IMPORTANCE: Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train su...