Retinal diseases can be manifested in optical coherence tomography (OCT) images since many signs of retina abnormalities are visible in OCT. Fluid regions can reveal the signs of age-related macular degeneration (AMD) and diabetic macular edema (DME) diseases and automatic segmentation of these regions can help ophthalmologists for diagnosis and treatment. This work presents a fully-automated method based on graph shortest path layer segmentation and fully convolutional networks (FCNs) for fluid segmentation. The proposed method has been evaluated on a dataset containing 600 OCT scans of 24 subjects. Results showed that the proposed FCN model outperforms 3 existing fluid segmentation methods by the improvement of 4.44% and 6.28% with respect to dice cofficients and sensitivity, respectively
Keywords: Fully Convolutional Networks, Fluid Segmentation, Graph Shortest Path, Retina, Optical Coherence Tomography