RAVIR: Retinal Artery/Vein Segmentation in IR

42 infrared reflectance fundus images with separate artery and vein segmentation masks. 23 train / 19 test.

At a glance

Field	Value
Short name	ravir
Full name	RAVIR: Retinal Artery/Vein Segmentation in IR
First published	2022-07
Publication date precision	month
Publication date evidence	ravirdataset.github.io/data
Publication date source field	Official project page: News
Publication date reviewed	2026-09-11
Primary category	fundus
Resource role	current_dataset
Dataset family	ravir
Contained modalities	fundus
Tasks	segmentation
Primary reported quantity	42 images
Classes	Not reported (Not reported)
Splits	train, test
Size	0.05 GB
Source-stated terms	Research only
Normalized terms	research-only
Descriptive screening label	Research or challenge restriction recorded; check source
Terms scope	dataset_files
Access friction	anonymous_direct
Route backend	Google Drive
Availability	available (checked 2026-07-21)
Acquisition support	loader_implemented_not_live_tested
Legacy sample-loader status	Standard loader included

Reported quantities

Role	Count	Unit	Scope	Basis	Evidence
Primary	42	images	Primary quantity reported in the reviewed catalog source	legacy_catalog_field	ravir.grand-challenge.org/data

Counts retain their source-reported units. Additional rows can describe components, paired items, or derivative copies and are not automatically added to the primary quantity.

Access information and download

CLI

# Read-only preflight
eyehub download ravir --data-dir ./data --dry-run --json

# Download, only when preflight reports supported behavior
eyehub download ravir --data-dir ./data

Python

from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY

ds = REGISTRY.get_dataset('ravir')
print(preflight_dataset(ds, './data'))  # no download

Upstream page: ravir.grand-challenge.org/data

Source-term evidence: ravir.grand-challenge.org/data

Loader example

This entry includes a standard DatasetSample loader.

from pathlib import Path
from eyedatahub.datasets.registry import REGISTRY

data_dir = Path('~/.eyedatahub/data').expanduser()
ds = REGISTRY.get_dataset('ravir')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{ravir,
  title  = { RAVIR: Retinal Artery/Vein Segmentation in IR },
  note   = { Hatamizadeh et al., 'RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging', JBHI 2022 },
  year   = { 2022 },
  url    = { https://ravir.grand-challenge.org/data/ },
}

Plain text

Hatamizadeh et al., 'RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging', JBHI 2022.

Source-stated terms

• Raw source string: Research only
• Normalized category: research-only
• Apparent scope: dataset_files
• Descriptive screening label: Research or challenge restriction recorded; check source

⚠️ Source-stated terms, scope, and normalized labels are curation metadata, not legal advice or a permission finding. Review the current official source before transfer or reuse.

Similar resources by shared modality

• airogs: AIROGS: AI for Robust Glaucoma Screening (113,893 images, cc-by-nc-nd)
• multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (103,959 images, mit)
• eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 question answer pairs, unknown)
• justraigs: JustRAIGS: Just Referral AI Glaucoma Screening Dataset (101,442 images, cc-by-nc-nd)
• eyepacs: EyePACS — Diabetic Retinopathy Detection (Kaggle 2015) (88,702 images, research-only)
• angioreport: AngioReport Fundus Angiography Report Dataset (55,361 images, unknown)
• ffa_ir: FFA-IR Medical Report Dataset (47,247 images, unknown)
• mfiddr: MFIDDR: Multi-Field Imaging Dataset for Diabetic Retinopathy (34,452 images, mit)