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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

FieldValue
Short nameravir
Full nameRAVIR: Retinal Artery/Vein Segmentation in IR
Primary categoryfundus
Contained modalitiesfundus
Taskssegmentation
Samples42
ClassesNot reported (Not reported)
Splitstrain, test
Size0.05 GB
Source-stated termsResearch only
Normalized termsresearch-only
Descriptive screening labelResearch or challenge restriction recorded; check source
Terms scopedataset_files
Access frictionanonymous_direct
Route backendGoogle Drive
Availabilityavailable (checked 2026-07-21)
Acquisition supportloader_implemented_not_live_tested
Legacy sample-loader statusStandard loader included

Access preflight and acquisition

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

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

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

@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/ },
}

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.

  • airogs: AIROGS: AI for Robust Glaucoma Screening (113,893 records, cc-by-nc-nd)
  • eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 records, unknown)
  • justraigs: JustRAIGS: Just Referral AI Glaucoma Screening Dataset (101,442 records, cc-by-nc-nd)
  • eyepacs: EyePACS — Diabetic Retinopathy Detection (Kaggle 2015) (88,702 records, research-only)
  • multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (58,036 records, mit)
  • angioreport: AngioReport Fundus Angiography Report Dataset (55,361 records, unknown)
  • ffa_ir: FFA-IR Medical Report Dataset (47,247 records, unknown)
  • bidr: BiDR: Diabetic Retinopathy Diagnosis Dataset (35,126 records, unknown)