We developed a fully automated system using a convolutional neural network (CNN) for total
retina segmentation in optical coherence tomography (OCT) that is robust to the presence of …

Manual assessment of the retinal thickness in optical coherence tomography images is a
time-consuming task, prone to error and inter-observer variability. The wide variability of the …

Introduction–Retinal layer segmentation in optical coherence tomography (OCT) images is
an important approach for detecting and prognosing disease. Automating segmentation …

Segmenting the retinal layers in optical coherence tomography (OCT) images helps to
quantify the layer information in early diagnosis of retinal diseases, which are the main …

Optical coherence tomography (OCT) is a high-resolution and noninvasive imaging modality
that has become one of the most prevalent techniques for ophthalmic diagnosis. Retinal …

Despite the existence of automatic segmentation techniques, trained graders still rely on
manual segmentation to provide retinal layers and features from clinical optical coherence …

The development of improved segmentation algorithms for more consistently accurate
detection of retinal boundaries is a potentially useful solution to the limitations of existing …

A novel statistical model based on texture and shape for fully automatic intraretinal layer
segmentation of normal retinal tomograms obtained by a commercial 800nm 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 …

Retinal layer thickness, evaluated as a function of spatial position from optical coherence
tomography (OCT) images is an important diagnostics marker for many retinal diseases …