  As a non-invasive imaging modality, optical coherence tomography (OCT) can
provide micrometer-resolution 3D images of retinal structures. Therefore it is
commonly used in the diagnosis of retinal diseases associated with edema in and
under the retinal layers. In this paper, a new framework is proposed for the
task of fluid segmentation and detection in retinal OCT images. Based on the
raw images and layers segmented by a graph-cut algorithm, a fully convolutional
neural network was trained to recognize and label the fluid pixels. Random
forest classification was performed on the segmented fluid regions to detect
and reject the falsely labeled fluid regions. The leave-one-out cross
validation experiments on the RETOUCH database show that our method performs
well in both segmentation (mean Dice: 0.7317) and detection (mean AUC: 0.985)
tasks.
