Rasti OCT Dataset (Noor Eye Hospital, Tehran)

148 Heidelberg Spectralis SD-OCT volumes (~4,254 B-scans) for 3-class volume-level classification: Normal (50 volumes), AMD (48 volumes), DME (50 volumes). Variable B-scans per volume: 19, 25, 31, or 61 slices. Acquired at Noor Eye Hospital, Tehran, Iran.

At a glance

Field	Value
Short name	rasti_oct
Full name	Rasti OCT Dataset (Noor Eye Hospital, Tehran)
Primary category	oct
Contained modalities	oct
Tasks	classification
Samples	4,254
Classes	3 (Normal, AMD, DME)
Splits	train
Size	2.0 GB
Source-stated terms	Research use 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	Manual (upstream-gated)
Availability	available (checked 2026-07-21)
Acquisition support	guided_instructions_only
Legacy sample-loader status	Standard loader included

Notes

Google Drive is password-protected (password: MCME2017). Download manually: Main archive: https://drive.google.com/file/d/1Rv82F7CjPveyONdy1YbRHh05emCb6_Eu DME labels: https://drive.google.com/file/d/1ocxB44TiiInE-jnt8Go6XQNmFwdTxOyN AMD labels: https://drive.google.com/file/d/1yaNiK40QL_s7fgMLM98l_F3TCMwFERnP Label files flag 'suspicious' B-scans (≥50% threshold) within each volume.

Access preflight and acquisition

CLI

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

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

Python

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

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

Upstream page: drive.google.com/file

Source-term evidence: drive.google.com/file

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

Citation

BibTeX

@misc{rasti_oct,
  title  = { Rasti OCT Dataset (Noor Eye Hospital, Tehran) },
  note   = { Rasti R et al., 'Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble', IEEE Transactions on Medical Imaging 37(4):1024–1034 (2018). doi:10.1109/TMI.2017.2780115 },
  year   = { 2018 },
  url    = { https://drive.google.com/file/d/1Rv82F7CjPveyONdy1YbRHh05emCb6_Eu },
}

Plain text

Rasti R et al., 'Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble', IEEE Transactions on Medical Imaging 37(4):1024–1034 (2018). doi:10.1109/TMI.2017.2780115

Source-stated terms

• Raw source string: Research use 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.

Related datasets with shared modalities

• syn_oct: SYN-OCT Synthetic Glaucoma OCT Dataset (200,000 records, cc-by)
• eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 records, unknown)
• 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)