Duke Srinivasan Retinal OCT Dataset

Forty-five retinal OCT volumes acquired with a Spectralis system: 15 dry AMD, 15 diabetic macular edema, and 15 normal volumes. The official Duke release provides the study data.

At a glance

Field	Value
Short name	oct_cirrus
Full name	Duke Srinivasan Retinal OCT Dataset
Primary category	oct
Contained modalities	oct
Tasks	classification
Samples	45
Classes	3 (AMD, DME, Normal)
Splits	all
Size	0.58 GB
Source-stated terms	Research only: research and educational use; commercialization and redistribution prohibited
Normalized terms	research-only
Descriptive screening label	Research or challenge restriction recorded; check source
Terms scope	dataset_files
Access friction	anonymous_direct
Route backend	Manual (upstream-gated)
Availability	available (checked 2026-07-21)
Acquisition support	guided_instructions_only
Legacy sample-loader status	Standard loader included

Notes

The source permits research and educational use but prohibits commercialization and redistribution. The sample count records 45 volumes; the loader emits their 3,231 B-scans.

Access preflight and acquisition

CLI

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

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

Python

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

ds = REGISTRY.get_dataset('oct_cirrus')
print(preflight_dataset(ds, './data'))  # no transfer

Upstream page: people.duke.edu/~sf59

Source-term evidence: people.duke.edu/~sf59

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

Citation

BibTeX

@misc{oct_cirrus,
  title  = { Duke Srinivasan Retinal OCT Dataset },
  note   = { Srinivasan PP, Kim LA, Mettu PS, et al. Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express. 2014;5:3568-3577. doi:10.1364/BOE.5.003568 },
  year   = { 2014 },
  url    = { https://people.duke.edu/~sf59/Srinivasan_BOE_2014_dataset.htm },
}

Plain text

Srinivasan PP, Kim LA, Mettu PS, et al. Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express. 2014;5:3568-3577. doi:10.1364/BOE.5.003568

Source-stated terms

• Raw source string: Research only: research and educational use; commercialization and redistribution prohibited
• 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.

Related datasets with shared modalities

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• kermany_oct: Kermany OCT 2018: Retinal OCT Image Classification (84,484 records, cc-by)
• multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (58,036 records, mit)
• lmod_plus: LMOD+ Multimodal Ophthalmology Benchmark (32,633 records, unknown)
• harvard_fairvision: Harvard-FairVision (AMD + DR + Glaucoma, paired SLO + OCT) (30,000 records, cc-by-nc-nd)
• mario: MARIO: AMD-Progression Longitudinal OCT (MICCAI 2024) (30,000 records, cc-by)
• mmrdr: MMRDR: Multi-Modal Retinal Diabetic Retinopathy Dataset (24,460 records, cc-by)