AIMC Topic: Retina

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Retinal vascular junction detection and classification via deep neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: The retinal fundus contains intricate vascular trees, some of which are mutually intersected and overlapped. The intersection and overlapping of retinal vessels represent vascular junctions (i.e. bifurcation and crossover) ...

Deep learning based retinal OCT segmentation.

Computers in biology and medicine
We look at the recent application of deep learning (DL) methods in automated fine-grained segmentation of spectral domain optical coherence tomography (OCT) images of the retina. We describe a new method combining fully convolutional networks (FCN) w...

DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images.

Physics in medicine and biology
Speckle is a major quality degrading factor in optical coherence tomography (OCT) images. In this work we propose a new deep learning network for speckle reduction in retinal OCT images, termed DeSpecNet. Unlike traditional algorithms, the model can ...

An Intelligent Segmentation and Diagnosis Method for Diabetic Retinopathy Based on Improved U-NET Network.

Journal of medical systems
Due to insufficient samples, the generalization performance of deep network is insufficient. In order to solve this problem, an improved U-net based image automatic segmentation and diagnosis algorithm was proposed, in which the max-pooling operation...

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.

Artificial intelligence in medicine
Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosi...

Convolutional Neural Networks for Spectroscopic Analysis in Retinal Oximetry.

Scientific reports
Retinal oximetry is a non-invasive technique to investigate the hemodynamics, vasculature and health of the eye. Current techniques for retinal oximetry have been plagued by quantitatively inconsistent measurements and this has greatly limited their ...

Retinal image assessment using bi-level adaptive morphological component analysis.

Artificial intelligence in medicine
The automated analysis of retinal images is a widely researched area which can help to diagnose several diseases like diabetic retinopathy in early stages of the disease. More specifically, separation of vessels and lesions is very critical as featur...

Automatic Cataract Classification Using Deep Neural Network With Discrete State Transition.

IEEE transactions on medical imaging
Cataract is the clouding of lens, which affects vision and it is the leading cause of blindness in the world's population. Accurate and convenient cataract detection and cataract severity evaluation will improve the situation. Automatic cataract dete...

Toward Safe Retinal Microsurgery: Development and Evaluation of an RNN-Based Active Interventional Control Framework.

IEEE transactions on bio-medical engineering
OBJECTIVE: Robotics-assisted retinal microsurgery provides several benefits including improvement of manipulation precision. The assistance provided to the surgeons by current robotic frameworks is, however, a "passive" support, e.g., by damping hand...

Self-supervised iterative refinement learning for macular OCT volumetric data classification.

Computers in biology and medicine
We present self-supervised iterative refinement learning (SIRL) as a pipeline to improve a type of macular optical coherence tomography (OCT) volumetric image classification algorithms. In this type of algorithms, first, two-dimensional (2D) image cl...