ArticleScientific data2022
FIVES: A Fundus Image Dataset for Artificial Intelligence based Vessel Segmentation.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 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
56 citing papers in PubMed.
- Automated optical coherence tomography quantification of retinal wall-to-lumen alterations in preeclampsia.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- An AI-Assisted Protocol for Quantifying Superficial Chorioallantoic Membrane Vasculature.Cells, tissues, organs · 2026Article
- Explicit Inclusion of Diabetes Mellitus Without Retinopathy Within Diabetic Retinopathy Prediction.Translational vision science & technology · 2026Article
- A generalist biomedical vision-language model via multi-CLIP knowledge distillation.Nature communications · 2026Article
- Retinal lesion annotation on fundus imaging: an interobserver variability study.Scientific reports · 2026Article
- LFU-Net: LoRA-enhanced frequency-aware U-Net with recursive residual attention fusion for retinal segmentation.Scientific reports · 2026Article
- Global-Token U-Net with Hybrid Loss for Trustworthy Medical Image Super-Resolution.Sensors (Basel, Switzerland) · 2026Article
- Automated diagnosis of plus form and early stages of ROP using deep learning models.Scientific reports · 2026Article
- Unified Framework for Enhancement of Low-Quality Fundus Images.Journal of imaging informatics in medicine · 2026Article
- Classifying retinal images via vascular-optic disc cross-segmentation and attentive feature selection.Scientific reports · 2026Article
- A MultiRater MultiOrgan Abdominal CT Dataset for Calibration Analysis and Uncertainty Modeling in Segmentation.Scientific data · 2026Article
- Morphology-aware distillation for lightweight retinal vessel segmentation across fundus photography and OCT angiography.Frontiers in cell and developmental biology · 2026Article
- Robust benchmarking of DeepLabv3 hybrid models for multiclass fundus screening and referable-disease triage on the FIVES dataset.Frontiers in medicine · 2026Article
- Artificial intelligence-based diagnostic model construction and quantitative analysis of retinal vein occlusion lesions using color fundus photographs.Frontiers in cell and developmental biology · 2026Article
- Reconstruction with a Custom-made 3D-printed Prosthesis for Limb Salvage and Joint Preservation after Resection of the Calf Myxofibrosarcoma Involving the Tibia: A Case Report and Literature Review.Current medical imaging · 2026Review
- Enhanced feature dynamic fusion gated UNet for robust retinal vessel segmentation.Scientific reports · 2025Article
- Code-Free Machine Learning for the Detection of Common Ophthalmic Diseases.Translational vision science & technology · 2025Article
- Confident Learning-Based Label Correction for Retinal Image Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
- VascX Models: Deep Ensembles for Retinal Vascular Analysis From Color Fundus Images.Translational vision science & technology · 2025Article
- Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal vasculature provides an opportunity for direct observation of vessel morphology, which is linked to multiple clinical conditions. However, objective and quantitative interpretation of the retinal vasculature relies on precise vessel segmentation, which is time consuming and labor intensive. Artificial intelligence (AI) has demonstrated great promise in retinal vessel segmentation. The development and evaluation of AI-based models require large numbers of annotated retinal images. However, the public datasets that are usable for this task are scarce. In this paper, we collected a color fundus image vessel segmentation (FIVES) dataset. The FIVES dataset consists of 800 high-resolution multi-disease color fundus photographs with pixelwise manual annotation. The annotation process was standardized through crowdsourcing among medical experts. The quality of each image was also evaluated. To the best of our knowledge, this is the largest retinal vessel segmentation dataset for which we believe this work will be beneficial to the further development of retinal vessel segmentation.
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.