| Fully Convolutional Networks for Fluid Segmentation |
| Paper ID : 1023-MVIP2020 |
| Authors: |
|
Abdolreza Rashno *1, Behnam Azimi2, Sadegh Fadaei3 1none,none 2Department of Computer Engineering Faculty of Engineering Lorestan University 3Department of Electrical Engineering Faculty of Engineering Yasouj University |
| 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 |
| Status : Paper Accepted (Oral Presentation) |
