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 retina...Show MoreMetadata
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.
Published in: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Date of Conference: 18-21 July 2018
Date Added to IEEE Xplore: 28 October 2018
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PubMed ID: 30441692