ArticleSensors (Basel, Switzerland)2023
Identifying the Edges of the Optic Cup and the Optic Disc in Glaucoma Patients by Segmentation.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- A Systematic Multi-Dataset, Multi-Seed Evaluation of Preprocessing Strategies for Retinal Optic Disc and Cup Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- DB-SegNet: optimized framework for glaucoma detection and optic structure segmentation from retinal fundus images.Scientific reports · 2025Article
- Automated cup-to-disc ratio quantification via color fundus photography for chronic glaucoma screening.BMC medical imaging · 2025Article
- Explainable Transformer-Based Framework for Glaucoma Detection from Fundus Images Using Multi-Backbone Segmentation and vCDR-Based Classification.Diagnostics (Basel, Switzerland) · 2025Article
- A novel method for optic disc localization using fast circlet transform and Chan-Vese segmentation.Scientific reports · 2025Article
- Optic Cup and Disc Segmentation of Fundus Images Using Artificial Intelligence Externally Validated With Optical Coherence Tomography Measurements.Translational vision science & technology · 2025Article
- Cup and Disc Segmentation in Smartphone Handheld Ophthalmoscope Images with a Composite Backbone and Double Decoder Architecture.Vision (Basel, Switzerland) · 2025Article
- Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network.Biomimetics (Basel, Switzerland) · 2024Article
- Deep Learning Technology and Image Sensing.Sensors (Basel, Switzerland) · 2024Article
- Artificial intelligence and glaucoma: a lucid and comprehensive review.Frontiers in medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
With recent advancements in artificial intelligence, fundus diseases can be classified automatically for early diagnosis, and this is an interest of many researchers. The study aims to detect the edges of the optic cup and the optic disc of fundus images taken from glaucoma patients, which has further applications in the analysis of the cup-to-disc ratio (CDR). We apply a modified U-Net model architecture on various fundus datasets and use segmentation metrics to evaluate the model. We apply edge detection and dilation to post-process the segmentation and better visualize the optic cup and optic disc. Our model results are based on ORIGA, RIM-ONE v3, REFUGE, and Drishti-GS datasets. Our results show that our methodology obtains promising segmentation efficiency for CDR analysis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.