DRIVE: Digital Retinal Images for Vessel Extraction

Forty color fundus photographs from a diabetic retinopathy screening program, divided into 20 training and 20 test images, with vessel reference annotations and field of view masks.

At a glance

Field	Value
Short name	`drive`
Full name	DRIVE: Digital Retinal Images for Vessel Extraction
First published	Unknown
Publication date precision	Unknown
Publication date evidence	Unknown
Publication date source field	Unknown
Publication date reviewed	Unknown
Primary category	`fundus`
Resource role	`current_dataset`
Dataset family	`drive`
Contained modalities	fundus
Tasks	segmentation
Primary reported quantity	40 images
Classes	2 (background, vessel)
Splits	train, test
Size	Not reported
Source-stated terms	Unknown; no named dataset license on the official page
Normalized terms	`unknown`
Descriptive screening label	Unknown or unclear; do not assume permission
Terms scope	`unknown`
Access friction	`self_service_authenticated`
Route backend	Manual (upstream-gated)
Availability	`available` (checked 2026-07-21)
Acquisition support	`guided_instructions_only`
Legacy sample-loader status	Metadata and access only

Reported quantities

Role	Count	Unit	Scope	Basis	Evidence
Primary	40	`images`	Primary quantity reported in the reviewed catalog source	`legacy_catalog_field`	drive.grand-challenge.org/DRIVE

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.

Notes

The official page reports 768 by 584 pixel images, one manual vessel segmentation for each training image, and hidden test references used by the evaluation server. A separate Dryad record provides a documented derived vessel-tracing annotation layer. Verify reuse terms with each source before redistribution.

Documented relationships

These links record source-supported lineage or overlap, not merely similar modality tags.

retinal_vessel_robustness is derived from this record: The robustness benchmark contains augmented versions of DRIVE, STARE, and CHASE_DB1 images. (evidence)
rite is derived from this record: RITE adds artery, vein, and vessel-tree labels to the same 40 DRIVE images. (evidence)

Access information and download

CLI
Python

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

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

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

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

Upstream page: drive.grand-challenge.org/DRIVE

Loader status

This catalog record provides metadata and access instructions, but it does not yet include a standard DatasetSample loader. Inspect the source file structure or contribute a loader before using it in a training pipeline.

Citation

BibTeX
Plain text

@misc{drive,
  title  = { DRIVE: Digital Retinal Images for Vessel Extraction },
  note   = { Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging. 2004;23:501-509. doi:10.1109/TMI.2004.825627 },
  year   = { 2004 },
  url    = { https://drive.grand-challenge.org/DRIVE/ },
}

Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging. 2004;23:501-509. doi:10.1109/TMI.2004.825627

Source-stated terms

Raw source string: Unknown; no named dataset license on the official page
Normalized category: unknown
Apparent scope: unknown
Descriptive screening label: Unknown or unclear; do not assume permission

⚠️ 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)

At a glance​

Reported quantities​

Notes​

Documented relationships​

Access information and download​

Other documented locations​

Loader status​

Citation​

Source-stated terms​

Similar resources by shared modality​