We developed a deep learning algorithm for the automatic segmentation and quantification
of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) …

Purpose Development and validation of a fully automated method to detect and quantify
macular fluid in conventional OCT images. Design Development of a diagnostic modality …

Cystoid macular edema (CME) is observed in a variety of ocular disorders and is strongly
associated with vision loss. Optical coherence tomography (OCT) provides excellent …

Retinal Fluids (fluid collections) develop because of the accumulation of fluid in the retina,
which may be caused by several retinal disorders, and can lead to loss of vision. Optical …

Optical coherence tomography (OCT) is an imaging modality that is used extensively for
ophthalmic diagnosis, near-histological visualization, and quantification of retinal …

Automated and accurate segmentation of cystoid structures in optical coherence
tomography (OCT) is of interest in the early detection of retinal diseases. It is, however, a …

Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid
Segmentation Network (ReF-Net), to segment retinal fluid in diabetic macular edema (DME) …

PURPOSE: To evaluate a new computerized segmentation technique for the quantification
of intraretinal and subretinal fluid in spectral-domain optical coherence tomography (SD …

Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by
high-resolution three-dimensional optical coherence tomography (OCT), which is used …

Purpose: To develop a reliable algorithm for the automated identification, localization, and
volume measurement of exudative manifestations in neovascular age-related macular …