AIMC Topic: Retina

Clear Filters Showing 351 to 360 of 482 articles

Structure-Preserving Guided Retinal Image Filtering and Its Application for Optic Disk Analysis.

IEEE transactions on medical imaging
Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degeneration, and diabetic retinopathy. With the development of computer science, computer aided diagnosis h...

Automatic Cone Photoreceptor Localisation in Healthy and Stargardt Afflicted Retinas Using Deep Learning.

Scientific reports
We present a robust deep learning framework for the automatic localisation of cone photoreceptor cells in Adaptive Optics Scanning Light Ophthalmoscope (AOSLO) split-detection images. Monitoring cone photoreceptors with AOSLO imaging grants an excell...

Antiproliferative and anti-apoptotic effect of astaxanthin in an oxygen-induced retinopathy mouse model.

Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
OBJECTIVE: To evaluate the impact of intravitreal (IV) and intraperitoneal (IP) astaxanthin (AST) injections on neovascular development (ND), retinal morphology, and apoptotic activity in a C57BL/6J mouse model with hyperoxia-induced retinopathy (HIR...

A machine learning approach for automated assessment of retinal vasculature in the oxygen induced retinopathy model.

Scientific reports
Preclinical studies of vascular retinal diseases rely on the assessment of developmental dystrophies in the oxygen induced retinopathy rodent model. The quantification of vessel tufts and avascular regions is typically computed manually from flat mou...

Segmentation of Intra-Retinal Cysts From Optical Coherence Tomography Images Using a Fully Convolutional Neural Network Model.

IEEE journal of biomedical and health informatics
Optical coherence tomography (OCT) is an imaging modality that is used extensively for ophthalmic diagnosis, near-histological visualization, and quantification of retinal abnormalities such as cysts, exudates, retinal layer disorganization, etc. Int...

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.

Nature biomedical engineering
Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be di...

Surrogate-Assisted Retinal OCT Image Classification Based on Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
Optical Coherence Tomography (OCT) is beco-ming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly r...

Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

Molecules (Basel, Switzerland)
The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpf...

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

Journal of medical systems
The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automa...

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

PloS one
Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNe...