dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis

Clicks: 243
ID: 243450
2018
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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.
Reference Key
al-bander2018symmetrydense Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Baidaa Al-Bander;Bryan M. Williams;Waleed Al-Nuaimy;Majid A. Al-Taee;Harry Pratt;Yalin Zheng
Journal journal of hospitality and tourism management
Year 2018
DOI
10.3390/sym10040087
URL
http://www.mdpi.com/2073-8994/10/4/87
Keywords
Deep learning image segmentation convolutional neural networks (cnns) glaucoma diagnosismathematics

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