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 …

Medical image segmentation is a fundamental and challenging problem for analyzing
medical images. Among different existing medical image segmentation methods, graph …

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 …

Retinal swelling due to the accumulation of fluid is associated with the most vision-
threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of …

We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers
on spectral domain optical coherence tomography images of eyes with diabetic macular …

Background In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate
estimation of multi-class retinal fluid (MRF) is required for the activity prescription and …

Liver segmentation is still a challenging task in medical image processing area due to the
complexity of the liver's anatomy, low contrast with adjacent organs, and presence of …

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) …

Drusen is considered as the landmark for diagnosis of AMD and important risk factor for the
development of AMD. Therefore, accurate segmentation of drusen in retinal OCT images is …

The manual segmentation of individual retinal layers within optical coherence tomography
(OCT) images is a time-consuming task and is prone to errors. The investigation into …