Name
Title | ||
|---|---|---|
Optic Disc Segmentation from Retinal Fundus Images via Deep Object Detection Networks. |
Abstract | ||
|---|---|---|
Accurate optic disc (OD) segmentation is a fundamental step in computer-aided ocular disease diagnosis. In this paper, we propose a new pipeline to segment OD from retinal fundus images based on deep object detection networks. The fundus image segmentation problem is redefined as a relatively more straightforward object detection task. This then allows us to determine the OD boundary simply by transforming the predicted bounding box into a vertical and non-rotated ellipse. Using Faster R-CNN as the object detector, our method achieves state-of-the-art OD segmentation results on ORIGA dataset, outperforming existing methods in this field. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8513592 | EMBC |
Field | DocType | Volume |
Computer vision,Object detection,Segmentation,Computer science,Fundus (eye),Optic disc,Image segmentation,Artificial intelligence,Ellipse,Detector,Minimum bounding box | Conference | 2018 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||