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Optic Disc Segmentation from Retinal Fundus Images via Deep Object Detection Networks | IEEE Conference Publication | IEEE Xplore

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 retina...Show More

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.
Date of Conference: 18-21 July 2018
Date Added to IEEE Xplore: 28 October 2018
ISBN Information:

ISSN Information:

PubMed ID: 30441692
Conference Location: Honolulu, HI, USA

References

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