Fully Convolutional Networks for Fluid Segmentation in Retina Images

Behnam Azimi, Abdolreza Rashno, Sadegh Fadaei
The 11th Iranian and the first International Conference on Machine Vision and Image Processing (MVIP 2020)

Abstract

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