This paper presents a novel U-Net model incorporating a hybrid attention mechanism for
automating the segmentation of sub-retinal layers in Optical Coherence Tomography (OCT) …

Deep learning methods have enabled a fast, accurate and automated approach for retinal
layer segmentation in posterior segment OCT images. Due to the success of semantic …

Retinal layer analysis on OCT images is a standard procedure used by ophthalmologists to
diagnose various diseases. Due to a large number of generated OCT images for each …

Purpose: To automate the segmentation of retinal layers, we propose DeepRetina, a method
based on deep neural networks. Methods: DeepRetina uses the improved Xception65 to …

Retinal layers segmentation in optical coherence tomography (OCT) images is a critical step
in the diagnosis of numerous ocular diseases. Automatic layers segmentation requires …

Optical coherence tomography (OCT) is a commonly-used method of extracting high
resolution retinal information. Moreover there is an increasing demand for the automated …

Abstract Images using OCT (Optical Coherence Tomography) for producing cross-sections
of images of retina light-sensitive tissue linings behind the human eye's black portions …

Optical coherence tomography (OCT)-based retinal imagery is often utilized to determine
influential factors in patient progression and treatment, for which the retinal layers of the …

Deep learning strategies, particularly convolutional neural networks (CNNs), are especially
suited to finding patterns in images and using those patterns for image classification. The …

Optical coherence tomography (OCT) is the standard method of generating high resolution
retinal images, which inform retinal disease diagnosis and guide management. However, in …