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Symmetry 2018, 10(4), 87; https://doi.org/10.3390/sym10040087

Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis

1
Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK
2
Department of Computer and Software Engineering, University of Diyala, 32010 Baqubah, Iraq
3
Department of Eye and Vision Science, Institute of Ageing and Chronic Disease, University of Liverpool, 6 West Derby Street, Liverpool L7 8TX, UK
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 11 February 2018 / Revised: 8 March 2018 / Accepted: 28 March 2018 / Published: 30 March 2018
(This article belongs to the Special Issue Advances in Medical Image Segmentation)
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Abstract

Glaucoma is a group of eye diseases which can cause vision loss by damaging the optic nerve. Early glaucoma detection is key to preventing vision loss yet there is a lack of noticeable early symptoms. Colour fundus photography allows the optic disc (OD) to be examined to diagnose glaucoma. Typically, this is done by measuring the vertical cup-to-disc ratio (CDR); however, glaucoma is characterised by thinning of the rim asymmetrically in the inferior-superior-temporal-nasal regions in increasing order. Automatic delineation of the OD features has potential to improve glaucoma management by allowing for this asymmetry to be considered in the measurements. Here, we propose a new deep-learning-based method to segment the OD and optic cup (OC). The core of the proposed method is DenseNet with a fully-convolutional network, whose symmetric U-shaped architecture allows pixel-wise classification. The predicted OD and OC boundaries are then used to estimate the CDR on two axes for glaucoma diagnosis. We assess the proposed method’s performance using a large retinal colour fundus dataset, outperforming state-of-the-art segmentation methods. Furthermore, we generalise our method to segment four fundus datasets from different devices without further training, outperforming the state-of-the-art on two and achieving comparable results on the remaining two. View Full-Text
Keywords: deep learning; image segmentation; convolutional neural networks (CNNs); dense network; retinal fundus images; glaucoma diagnosis deep learning; image segmentation; convolutional neural networks (CNNs); dense network; retinal fundus images; glaucoma diagnosis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Al-Bander, B.; Williams, B.M.; Al-Nuaimy, W.; Al-Taee, M.A.; Pratt, H.; Zheng, Y. Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis. Symmetry 2018, 10, 87.

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