Automated segmentation of object boundaries or surfaces is crucial for quantitative image
analysis in numerous biomedical applications. For example, retinal surfaces in optical …

Optical coherence tomography (OCT) is a noninvasive imaging modality with micrometer
resolution which has been widely used for scanning the retina. Retinal layers are important …

A major goal of analyzing retinal optical coherence tomography (OCT) images is retinal
layer segmentation. Accurate automated algorithms for segmenting smooth continuous layer …

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

Accurate quantitative analysis of the retinal layer in optical coherence tomography (OCT)
images plays a crucial role in detecting and diagnosing ocular diseases. In this paper, we …

We present a novel framework combining convolutional neural networks (CNN) and graph
search methods (termed as CNN-GS) for the automatic segmentation of nine layer …

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology
and used daily in the clinic. Automatic evaluation of such datasets requires an accurate …

Optical coherence tomography is routinely used clinically for the detection and management
of ocular diseases as well as in research where the studies may involve animals. This …

Optical coherence tomography (OCT) has proven to be an essential imaging modality for
ophthalmology and is proving to be very important in neurology. OCT enables high …

Optical coherence tomography (OCT) is a noninvasive imaging modality that can be used to
obtain depth images of the retina. Patients with multiple sclerosis (MS) have thinning retinal …