Optic Disc Segmentation from Retinal Fundus Images via Deep Object Detection Networks

Xu Sun; Yanwu Xu; Wei Zhao; Tianyuan You; Jiang Liu

doi:10.1109/EMBC.2018.8513592

Optic Disc Segmentation from Retinal Fundus Images via Deep Object Detection Networks

Xu Sun
, Yanwu Xu
, Wei Zhao
, Tianyuan You
, Jiang Liu

Research output: Journal Publication › Article › peer-review

32 Citations (Scopus)

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.

Original language	English
Pages (from-to)	5954-5957
Number of pages	4
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	2018
DOIs	https://doi.org/10.1109/EMBC.2018.8513592
Publication status	Published - 1 Jul 2018
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2018.8513592

Cite this

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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.",

author = "Xu Sun and Yanwu Xu and Wei Zhao and Tianyuan You and Jiang Liu",

year = "2018",

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language = "English",

volume = "2018",

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AU - Sun, Xu

AU - Xu, Yanwu

AU - Zhao, Wei

AU - You, Tianyuan

AU - Liu, Jiang

PY - 2018/7/1

Y1 - 2018/7/1

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85056647484

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EP - 5957

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

ER -

Optic Disc Segmentation from Retinal Fundus Images via Deep Object Detection Networks

Abstract

UN SDGs

ASJC Scopus subject areas

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