Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network.

Abstract

As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. These images can help reveal disease-related alterations below the surface of the retina, such as the presence of edema, or accumulation of fluid which can distort vision, and are an indication of disruptions in the vasculature of the retina. In this paper, a new framework is proposed for multiclass fluid segmentation and detection in the retinal OCT images. Based on the intensity of OCT images and retinal layer segmentations provided 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 proposed framework won the first place in the MICCAI RETOUCH challenge in 2017 on both the segmentation performance (mean Dice: 0.7667) and the detection performance (mean AUC: 1.00) tasks.