FIVES: Fundus Image Vessel Segmentation

800 high-resolution (2048x2048) fundus images with pixel-wise vessel segmentation. Covers normal, DR, AMD, glaucoma.

At a glance

Field	Value
Short name	fives
Full name	FIVES: Fundus Image Vessel Segmentation
First published	2022-05-01
Publication date precision	day
Publication date evidence	api.figshare.com/v2
Publication date source field	published_date (Figshare version 1)
Publication date reviewed	2026-09-11
Primary category	fundus
Resource role	current_dataset
Dataset family	fives
Contained modalities	fundus
Tasks	segmentation
Primary reported quantity	800 images
Classes	Not reported (Not reported)
Splits	train, test
Size	1.1 GB
Source-stated terms	CC BY 4.0
Normalized terms	cc-by
Descriptive screening label	Standard label without an explicit NC clause; not a permission finding
Terms scope	dataset_files
Access friction	self_service_authenticated
Route backend	Figshare
Availability	available (checked 2026-07-21)
Acquisition support	transfer_tested_partial
Legacy sample-loader status	Standard loader included

Reported quantities

Role	Count	Unit	Scope	Basis	Evidence
Primary	800	images	Primary quantity reported in the reviewed catalog source	legacy_catalog_field	https://doi.org/10.6084/m9.figshare.19688169

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.

Documented relationships

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

• mm_retinal_reason is derived from this record: The version-pinned official dataset card lists this record among the CFP or OCT sources used to construct MM-Retinal-Reason. (evidence)
• multieye is derived from this record: The MultiEYE paper names this record as one of the public fundus or OCT sources assembled for the benchmark. (evidence)
• smdg is derived from this record: The official SMDG source table lists this catalog record among the 19 standardized source domains. (evidence)

Access information and download

CLI

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

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

Python

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

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

Upstream page: https://doi.org/10.6084/m9.figshare.19688169

Source-term evidence: https://doi.org/10.6084/m9.figshare.19688169

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('fives')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{fives,
  title  = { FIVES: Fundus Image Vessel Segmentation },
  note   = { Jin et al., 'FIVES: A Fundus Image Dataset for Artificial Intelligence based Vessel Segmentation', Scientific Data 2022 },
  year   = { 2022 },
  url    = { https://doi.org/10.6084/m9.figshare.19688169 },
}

Plain text

Jin et al., 'FIVES: A Fundus Image Dataset for Artificial Intelligence based Vessel Segmentation', Scientific Data 2022.

Source-stated terms

• Raw source string: CC BY 4.0
• Normalized category: cc-by
• Apparent scope: dataset_files
• Descriptive screening label: Standard label without an explicit NC clause; not a permission finding

⚠️ 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)