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

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

    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 ...

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