AIMC Topic: Retina

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Enhancing retinal images in low-light conditions using semidecoupled decomposition.

Medical & biological engineering & computing
Eye diseases that are common and many diseases that result in visual ailments, such as diabetes and vascular disease, can be diagnosed through retinal imaging. The enhancement of retinal images often helps in diagnosing diseases related to retinal or...

Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed graph search.

Computers in biology and medicine
Analysis of the vascular tree is the basic premise to automatically diagnose retinal biomarkers associated with ophthalmic and systemic diseases, among which accurate identification of intersection and bifurcation points is quite challenging but impo...

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends.

Journal of healthcare engineering
Diabetic retinopathy (DR) is a common eye retinal disease that is widely spread all over the world. It leads to the complete loss of vision based on the level of severity. It damages both retinal blood vessels and the eye's microscopic interior layer...

Image processing and supervised machine learning for retinal microglia characterization in senescence.

Methods in cell biology
The process of senescence impairs the function of cells and can ultimately be a key factor in the development of disease. With an aging population, senescence-related diseases are increasing in prevalence. Therefore, understanding the mechanisms of c...

Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings.

Scientific reports
Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic ret...

Prediction of Visual Impairment in Epiretinal Membrane and Feature Analysis: A Deep Learning Approach Using Optical Coherence Tomography.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
PURPOSE: The aim was to develop a deep learning model for predicting the extent of visual impairment in epiretinal membrane (ERM) using optical coherence tomography (OCT) images, and to analyze the associated features.

Context encoder transfer learning approaches for retinal image analysis.

Computers in biology and medicine
During the last years, deep learning techniques have emerged as powerful alternatives to solve biomedical image analysis problems. However, the training of deep neural networks usually needs great amounts of labeled data to be done effectively. This ...

Robot-assisted subretinal injection system: development and preliminary verification.

BMC ophthalmology
BACKGROUND: To design and develop a surgical robot capable of assisting subretinal injection.

Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, AMD is the most frequent cause of blindness in developed countries. Although some...

MC-UNet: Multimodule Concatenation Based on U-Shape Network for Retinal Blood Vessels Segmentation.

Computational intelligence and neuroscience
Accurate retinal blood vessels segmentation is an important step in the clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and un...