Automatic CDR Estimation for Early Glaucoma Diagnosis

Fernández-Granero, Miguel Ángel; Sarmiento, Auxiliadora; Morillo, Daniel; Jiménez, Soledad Ballesteros; Alemany, P.; Fondón, Irene

doi:10.1155/2017/5953621

Cited by 16 publications

(6 citation statements)

References 57 publications

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“…22 Various new AA systems are being developed for possible future use in glaucoma screening. [23][24][25] Each system employs different techniques for glaucoma detection on colour fundus photographs. Some used OCT imaging for CDR calculation while another used a combination of structural and non-structural features for glaucoma detection.…”

Section: Discussionmentioning

confidence: 99%

Reliability of Graders and Comparison with an Automated Algorithm for Vertical Cup-Disc Ratio Grading in Fundus Photographs

Tong

Romero

Lim

et al. 2019

Ann Acad Med Singap

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Introduction: We aimed to investigate the intergrader and intragrader reliability of human graders and an automated algorithm for vertical cup-disc ratio (CDR) grading in colour fundus photographs. Materials and Methods: Two-hundred fundus photographs were selected from a database of 3000 photographs of patients screened at a tertiary ophthalmology referral centre. The graders included glaucoma specialists (n = 3), general ophthalmologists (n = 2), optometrists (n = 2), family physicians (n = 2) and a novel automated algorithm (AA). In total, 2 rounds of CDR grading were held for each grader on 2 different dates, with the photographs presented in random order. The CDR values were graded as 0.1-1.0 or ungradable. The grading results of the 2 senior glaucoma specialists were used as the reference benchmarks for comparison. Results: The intraclass correlation coefficient values ranged from 0.37-0.74 and 0.47-0.97 for intergrader and intragrader reliability, respectively. There was no significant correlation between the human graders’ level of reliability and their years of experience in grading CDR (P = 0.91). The area under the curve (AUC) value of the AA was 0.847 (comparable to AUC value of 0.876 for the glaucoma specialist). Bland Altman plots demonstrated that the AA’s performance was at least comparable to a glaucoma specialist. Conclusion: The results suggest that AA is comparable to and may have more consistent performance than human graders in CDR grading of fundus photographs. This may have potential application as a screening tool to help detect asymptomatic glaucoma-suspect patients in the community. Key words: Eyes, Glaucoma, Intraocular pressure

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Section: Discussionmentioning

confidence: 99%

Reliability of Graders and Comparison with an Automated Algorithm for Vertical Cup-Disc Ratio Grading in Fundus Photographs

Tong

Romero

Lim

et al. 2019

Ann Acad Med Singap

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“…In M. A. Fernandes Granero et. al [3] CDR is used for glaucoma diagnosis. The ROI is cropped and they used image derivatives to identify optic disc and cup boundaries and the model is evaluated using six classifiers namely, Simple tree (ST), Bagged tree (BT), Complex tree (CT), Linear discriminant (LD), Quadratic discriminant (QD), and KNN.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Glaucoma Detection Using Computer Vision

Sreemol¹,

Umesh²

2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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Glaucoma is an eye disorder that impairs the optic nerve and can cause permanent blindness if left untreated. This condition arises due to elevated pressure inside the retina. As damage from glaucoma is impossible to rectify, early detection helps to prevent vision loss. Manual examination of fundus photographs is a difficult process for medical examiners because a large set of images will have only a small number of glaucomatous images, so Computer-aided systems can reduce the work load by automatically analyzing the images. Here a computer-aided diagnosis system for glaucoma screening based on VCDR value along with texture features extracted using Gabor filter is proposed. The processed retinal fundus images are segmented and the VCDR value is calculated from the segmented images. The simulation is carried out in Python 3 and the databases used are DRISHTI GS1 and HRF. The system is evaluated using four classifiers SVM, KNN, Logistic regression and Random forest.

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“…Compared with OCT, colour fundus image is less expensive and more frequently used for detecting glaucoma. The optic cup (OC) to optic disc (OD) ratio (CDR) of fundus images is an important indicator in the screening and diagnosis of glaucoma [9]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening

Sun

Zhang

et al. 2021

Neural Comput & Applic

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Automatic segmentation of optic disc (OD) and optic cup (OC) is an essential task for analysing colour fundus images. In clinical practice, accurate OD and OC segmentation assist ophthalmologists in diagnosing glaucoma. In this paper, we propose a unified convolutional neural network, named ResFPN-Net, which learns the boundary feature and the inner relation between OD and OC for automatic segmentation. The proposed ResFPN-Net is mainly composed of multi-scale feature extractor, multi-scale segmentation transition and attention pyramid architecture. The multi-scale feature extractor achieved the feature encoding of fundus images and captured the boundary representations. The multi-scale segmentation transition is employed to retain the features of different scales. Moreover, an attention pyramid architecture is proposed to learn rich representations and the mutual connection in the OD and OC. To verify the effectiveness of the proposed method, we conducted extensive experiments on two public datasets. On the Drishti-GS database, we achieved a Dice coefficient of 97.59%, 89.87%, the accuracy of 99.21%, 98.77%, and the Averaged Hausdorff distance of 0.099, 0.882 on the OD and OC segmentation, respectively. We achieved a Dice coefficient of 96.41%, 83.91%, the accuracy of 99.30%, 99.24%, and the Averaged Hausdorff distance of 0.166, 1.210 on the RIM-ONE database for OD and OC segmentation, respectively. Comprehensive results show that the proposed method outperforms other competitive OD and OC segmentation methods and appears more adaptable in cross-dataset scenarios. The introduced multi-scale loss function achieved significantly lower training loss and higher accuracy compared with other loss functions. Furthermore, the proposed method is further validated in OC to OD ratio calculation task and achieved the best MAE of 0.0499 and 0.0630 on the Drishti-GS and RIM-ONE datasets, respectively. Finally, we evaluated the effectiveness of the glaucoma screening on Drishti-GS and RIM-ONE datasets, achieving the AUC of 0.8947 and 0.7964. These results proved that the proposed ResFPN-Net is effective in analysing fundus images for glaucoma screening and can be applied in other relative biomedical image segmentation applications.

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Automatic CDR Estimation for Early Glaucoma Diagnosis

Cited by 16 publications

References 57 publications

Reliability of Graders and Comparison with an Automated Algorithm for Vertical Cup-Disc Ratio Grading in Fundus Photographs

Reliability of Graders and Comparison with an Automated Algorithm for Vertical Cup-Disc Ratio Grading in Fundus Photographs

A Novel Method for Glaucoma Detection Using Computer Vision

Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening

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