Deep-learning based retinal fluid segmentation in optical coherence tomography images using a cascade of ENets

Abstract

Optical coherence tomography (OCT) is a non-invasive, painless and reproducible examination which allows ophthalmologists to visualize retinal layers. This imaging modality is useful to detect diseases such as diabetic macular edema (DME) or age related macular degeneration (AMD), which are associated with fluid accumulations. In this paper, a cascade of deep convolutional neural networks is proposed using ENets for the segmentation of fluid accumulations in OCT B-Scans. After denoising the B-Scans, a first ENet extracts the region of interest (ROI) between the inner limiting membrane (ILM) and the Bruch’s membrane (BM), whereas the second ENet segments the fluid in the ROI. A random forest classifier was applied on the segmented fluid regions to reject false positive. Our framework was trained on three different datasets with several diseases such as diabetic retinopathy (DR) and AMD. Our method achieves an average Dice Score for fluid segmentation of 0.80, 0.83 and 0.83 on the UMN DME, UMN AMD and Kermany datasets respectively.