ArticleScientific reports2019
Rim-to-Disc Ratio Outperforms Cup-to-Disc Ratio for Glaucoma Prescreening.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 49 citations in OpenAlex.
- Differentiating stages of functional vision loss from glaucoma using the Disc Damage Likelihood Scale and cup:disc ratio.The British journal of ophthalmology · 2024Trial
- Clinically aligned artificial intelligence for glaucoma diagnosis: enhancing retinal nerve fibre layer interpretation from fundus images.Eye (London, England) · 2026Article
- Optic Cup and Disc Segmentation of Fundus Images Using Artificial Intelligence Externally Validated With Optical Coherence Tomography Measurements.Translational vision science & technology · 2025Article
- DDLSNet: A Novel Deep Learning-Based System for Grading Funduscopic Images for Glaucomatous Damage.Ophthalmology science · 2023Article
- Analysis of the Asymmetry between Both Eyes in Early Diagnosis of Glaucoma Combining Features Extracted from Retinal Images and OCTs into Classification Models.Sensors (Basel, Switzerland) · 2023Article
- RimNet: A Deep Neural Network Pipeline for Automated Identification of the Optic Disc Rim.Ophthalmology science · 2023Article
- Chákṣu: A glaucoma specific fundus image database.Scientific data · 2023Article
- Comparative analysis of retinal photoplethysmographic spatial maps and thickness of retinal nerve fiber layer.PloS one · 2023Article
- Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?Journal of clinical medicine · 2022Article
- PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment.Scientific data · 2022Article
- A deep dive into the latest European Glaucoma Society and Asia-Pacific Glaucoma Society guidelines and their relevance to India.Indian journal of ophthalmology · 2022Review
- Spotlight on the Disc-Damage Likelihood Scale (DDLS).Clinical ophthalmology (Auckland, N.Z.) · 2021Review
- A Retrospective Comparison of Deep Learning to Manual Annotations for Optic Disc and Optic Cup Segmentation in Fundus Photographs.Translational vision science & technology · 2020Article
- Automatic analysis of normative retinal oximetry images.PloS one · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a novel and fully automated fundus image processing technique for glaucoma prescreening based on the rim-to-disc ratio (RDR). The technique accurately segments the optic disc and optic cup and then computes the RDR based on which it is possible to differentiate a normal fundus from a glaucomatous one. The technique performs a further categorization into normal, moderate, or severely glaucomatous classes following the disc-damage-likelihood scale (DDLS). To the best of our knowledge, this is the first engineering attempt at using RDR and DDLS to perform glaucoma severity assessment. The segmentation of the optic disc and cup is based on the active disc, whose parameters are optimized to maximize the local contrast. The optimization is performed efficiently by means of a multiscale representation, accelerated gradient-descent, and Green's theorem. Validations are performed on several publicly available databases as well as data provided by manufacturers of some commercially available fundus imaging devices. The segmentation and classification performance is assessed against expert clinician annotations in terms of sensitivity, specificity, accuracy, Jaccard, and Dice similarity indices. The results show that RDR based automated glaucoma assessment is about 8% to 10% more accurate than a cup-to-disc ratio (CDR) based system. An ablation study carried out considering the ground-truth expert outlines alone for classification showed that RDR is superior to CDR by 5.28% in a two-stage classification and about 3.21% in a three-stage severity grading.
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