ArticleJournal of digital imaging2019
Towards Accurate Segmentation of Retinal Vessels and the Optic Disc in Fundoscopic Images with Generative Adversarial Networks.
Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis.Journal of medical Internet research · 2021Pooled it
- Combined diagnostic accuracy of two artificial intelligence systems for glaucoma diagnosis using color fundus photography.PloS one · 2026Article
- Improving retinal vessel assessment precision by integrating deep learning with interactive editing and graphical modeling.Scientific reports · 2025Article
- Low rank based FAZ segmentation in OCTA images.Scientific reports · 2025Article
- Digital twin-driven operational CycleGAN-based multiple virtual-physical mappings for remaining useful life prediction under limited life cycle data.Journal of advanced research · 2025Article
- (DA-U)BMC ophthalmology · 2025Article
- Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review.Sensors (Basel, Switzerland) · 2024Review
- Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney.Scientific reports · 2024Article
- Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network.Biomimetics (Basel, Switzerland) · 2024Article
- Physics-informed deep generative learning for quantitative assessment of the retina.Nature communications · 2024Article
- Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography.Research square · 2024Article
- Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism.PeerJ. Computer science · 2024Article
- DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation.Journal of digital imaging · 2023Article
- Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data.Translational vision science & technology · 2023Article
- Placental Vessel Segmentation Using Pix2pix Compared to U-Net.Journal of imaging · 2023Article
- CCS-UNet: a cross-channel spatial attention model for accurate retinal vessel segmentation.Biomedical optics express · 2023Article
- Recent Advances in Deep Learning and Medical Imaging for Head and Neck Cancer Treatment: MRI, CT, and PET Scans.Cancers · 2023Review
- An interpretable and interactive deep learning algorithm for a clinically applicable retinal fundus diagnosis system by modelling finding-disease relationship.Scientific reports · 2023Article
- Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs.Diagnostics (Basel, Switzerland) · 2022Article
- Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods.Sensors (Basel, Switzerland) · 2022Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic segmentation of the retinal vasculature and the optic disc is a crucial task for accurate geometric analysis and reliable automated diagnosis. In recent years, Convolutional Neural Networks (CNN) have shown outstanding performance compared to the conventional approaches in the segmentation tasks. In this paper, we experimentally measure the performance gain for Generative Adversarial Networks (GAN) framework when applied to the segmentation tasks. We show that GAN achieves statistically significant improvement in area under the receiver operating characteristic (AU-ROC) and area under the precision and recall curve (AU-PR) on two public datasets (DRIVE, STARE) by segmenting fine vessels. Also, we found a model that surpassed the current state-of-the-art method by 0.2 - 1.0% in AU-ROC and 0.8 - 1.2% in AU-PR and 0.5 - 0.7% in dice coefficient. In contrast, significant improvements were not observed in the optic disc segmentation task on DRIONS-DB, RIM-ONE (r3) and Drishti-GS datasets in AU-ROC and AU-PR.
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.