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

Al-Bander, Baidaa; Williams, Bryan; Al-Nuaimy, Waleed; Al-Taee, Majid A.; Pratt, Harry; Zheng, Yalin

doi:10.3390/sym10040087

Cited by 137 publications

(95 citation statements)

References 49 publications

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“…The recurrent block increases the network depth while keeping the number of adjustable parameters unchanged through weight sharing. This is consistent with the purpose of the current CNN architecture [5][6][7]: Using relatively few parameters for more in-depth research. At the same time, we further explored the PLOS ONE spatial pyramid model and applied it to RFC-Net.…”

Section: Introductionsupporting

confidence: 79%

Joint disc and cup segmentation based on recurrent fully convolutional network

et al. 2020

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The optic disc(OD) and the optic cup(OC) segmentation is an key step in fundus medical image analysis. Previously, FCN-based methods have been proposed for medical image segmentation tasks. However, the consecutive convolution and pooling operations usually hinder dense prediction tasks which require detailed spatial information, such as image segmentation. In this paper, we propose a network called Recurrent Fully Convolution Network (RFC-Net) for automatic joint segmentation of the OD and the OC, which can captures more high-level information and subtle edge information. The RFC-Net can minimize the loss of spatial information. It is mainly composed of multi-scale input layer, recurrent fully convolutional network, multiple output layer and polar transformation. In RFC-Net, the multi-scale input layer constructs an image pyramid. We propose four recurrent units, which are respectively applied to RFC-Net. Recurrent convolution layer effectively ensures feature representation for OD and OC segmentation tasks through feature accumulation. For each multiple output image, the multiple output cross entropy loss function is applied. To better balance the cup ratio of the segmented image, the polar transformation is used to transform the fundus image from the cartesian coordinate system to the polar coordinate system. We evaluate the effectiveness and generalization of the proposed method on the DRISHTI-GS1 dataset. Compared with the original FCN method and other state-of-the-art methods, the proposed method achieves better segmentation performance.

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Section: Introductionsupporting

confidence: 79%

Joint disc and cup segmentation based on recurrent fully convolutional network

et al. 2020

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“…Current literature in OD/OC segmentation make use of classical overlap metrics such as the intersectionover-union (IoU, also known as Jaccard index) (Al-Bander et al, 2018;Edupuganti et al, 2018;Fu et al, 2018;Lim et al, 2015;Sevastopolsky, 2017;Sevastopolsky et al, 2018;Sun et al, 2018;Zilly et al, 2015) and the Dice index (Al-Bander et al, 2018;Edupuganti et al, 2018;Sevastopolsky, 2017;Sevastopolsky et al, 2018;Sun et al, 2018;Zilly et al, 2015). Although different by definition, these two metrics can be computed from each other, as they are defined as ratios of overlap between the predicted area and the manual reference annotation (Taha and Hanbury, 2015).…”

Section: Metricsmentioning

confidence: 99%

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

Orlando

Breda

et al. 2020

Medical Image Analysis

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Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations.Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders.However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MICCAI 2018. The challenge consisted * Corresponding authors: Yanwu Xu (ywxu@ieee.org) and Xiulan Zhang (

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“…The FCN is the first DL-based image segmentation method. Its merit lies in the realization of end-to-end segmentation [19,20]. However, the details of FCN image segmentation are not good enough, for failing to consider the spatial correlation between pixels.…”

Section: Structure Of U-net Modelmentioning

confidence: 99%

A Deep Learning Model for Striae Identification in End Images of Float Glass

Jin¹,

Xu²,

Tong³

et al. 2020

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For float glass, there is a correlation between the striae in end image and the manufacturing process. If clearly understood, the correlation helps to optimize and fine-tune the manufacturing process of float glass. This paper attempts to extract the striae from the end image of float glass with deep learning (DL) neural network (NN). For this purpose, an image segmentation model was established based on improved U-Net, a fully convolutional network (FCN), and used to accurately divide the glass liquid on the end image into different layers. Firstly, the improved U-Net model was constructed to extract the striae from each liquid layer on the end image. Next, the activation function and convolutional mode of the improved U-Net model were optimized to enhance the segmentation accuracy and shorten the training/prediction time. Finally, the proposed model was tested on the float glass production line of Hebei CSG Glass Co., Ltd. The test results show that our model achieved an accuracy of 94%. The research findings lay a solid basis for striae identification on end image of float glass, and provide guidance for optimization and fine-tuning of float glass manufacturing process.

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

Cited by 137 publications

References 49 publications

Joint disc and cup segmentation based on recurrent fully convolutional network

Joint disc and cup segmentation based on recurrent fully convolutional network

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

A Deep Learning Model for Striae Identification in End Images of Float Glass

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