Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning

Edupuganti, Venkata Gopal; Chawla, Akshay; Kale, Amit

doi:10.1109/icip.2018.8451753

Cited by 54 publications

(38 citation statements)

References 15 publications

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“…Most of existing methods use a surrogate segmentation/detection approach to first localize the ONH area and them crop the images around it (Edupuganti et al, 2018;Fu et al, 2018;Lim et al, 2015;Sevastopolsky, 2017;Zilly et al, 2015). This prevents false positive detections in regions with e.g.…”

Section: Optic Disc/cup Segmentationmentioning

confidence: 99%

“…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%

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

Orlando

Breda

et al. 2020

Medical Image Analysis

510

248

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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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Section: Optic Disc/cup Segmentationmentioning

confidence: 99%

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

510

248

View full text Add to dashboard Cite

show abstract

“…Exact detection of OC boundary is also a difficult task due to blood vessels present in the layer beneath retina [9]. In some cases, noise can also be introduced in segmented images in the form of extra information that needs to be eliminated [10]. To cope with all these problems, existing studies have added pre-processing and post-processing stages along with segmentation.…”

Section: Introductionmentioning

confidence: 99%

CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

et al. 2020

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Glaucoma is an eye disease that can cause loss of vision by damaging the optic nerve. It is the world's second leading cause of blindness after cataracts. Early diagnosis of glaucoma is a key to prevent permanent blindness as it has no noticeable symptoms in its early stages. Color fundus photography is used for examining the optic disc (OD) which is an important step in the diagnoses of glaucoma. This is done by estimating the cup-to-disc ratio (CDR). In this paper, we proposed a Cup Disc Encoder Decoder Network (CDED-Net) for the joint segmentation of optic disc (OD) and optic cup (OC). We have eradicated the preprocessing and post-processing steps to reduce the computational cost of the overall system. Segmentation of (OD) and OC is modeled as a semantic pixel-wise labeling problem. The model was trained on the DRISHTI-GS, RIM-ONE and REFUGE datasets. Experiments show that our CDED-Net system achieves state-of-the-art OD and OC segmentation results on these datasets.

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“…It is also called low-tension glaucoma [8]. In this case, the optic nerve gets affected even though there is no much pressure on the eye.…”

Section: Normal-tension Glaucomamentioning

confidence: 99%

A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma

Veena¹,

Muruganandham

Kumaran³

2020

SN Appl. Sci.

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Glaucoma is one of the leading severe retinal disease which damages the optic nerve head on the retinal part of the eye irreversibly. Once the person is diagnosed with glaucoma, it cannot be treated entirely, but it can be controlled. If glaucoma is not diagnosed in time, it will lead to vision loss by damaging the Optic Nerve Head. The glaucoma detection is performed based on the optic disc and optic cup parameters on the retinal part of the eye. In the existing system, many image processing and machine learning techniques used for the segmentation and classification of optic disc and optic cup. To improve the precision of diagnosis, the existing techniques used need an improvement. This article helps readers with more information about the existing methods applied for the diagnosis of glaucoma, it also lists the research gaps and technical challenges to improve the accuracy of segmentation and classification methods.

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Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning

Cited by 54 publications

References 15 publications

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

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

CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma

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