Fully Convolutional Networks for Fluid Segmentation
Paper ID : 1023-MVIP2020
Abdolreza Rashno *1, Behnam Azimi2, Sadegh Fadaei3
2Department of Computer Engineering Faculty of Engineering Lorestan University
3Department of Electrical Engineering Faculty of Engineering Yasouj University
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.
Fully Convolutional Networks, Fluid Segmentation, graph shortest path
Status : Paper Accepted (Oral Presentation)