iChallenge OCT Datasets (HDMILab / OMIA Workshops)

OCT challenge datasets from HDMILab covering retinal layer segmentation and fluid detection tasks from MICCAI/OMIA workshops. Includes sub-challenges such as AMD/CSC/DR classification and retinal layer segmentation; GAMMA challenge provides OCT volumes from 300 glaucoma patients (fundus + 3D OCT).

At a glance

Field	Value
Short name	ichallenge_oct
Full name	iChallenge OCT Datasets (HDMILab / OMIA Workshops)
Primary category	oct
Contained modalities	oct
Tasks	segmentation, classification
Samples	300
Classes	Not reported (Not reported)
Splits	train, test
Size	2.0 GB
Source-stated terms	Non-commercial research (challenge-specific)
Normalized terms	research-only
Descriptive screening label	Research or challenge restriction recorded; check source
Terms scope	challenge_participation
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	Standard loader included

Notes

num_samples refers to patient volumes (GAMMA has 300 patients, each with 3D OCT + fundus). Slice counts depend on sub-challenge. Visit http://hdmilab.cn/ichallenge, register/log in, and download the relevant challenge dataset(s). Place extracted files under ~/.eyedatahub/data/ichallenge_oct/.

Access preflight and acquisition

CLI

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

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

Python

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

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

Upstream page: hdmilab.cn/ichallenge

Source-term evidence: hdmilab.cn/ichallenge

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

Citation

BibTeX

@misc{ichallenge_oct,
  title  = { iChallenge OCT Datasets (HDMILab / OMIA Workshops) },
  note   = { HDMILab iChallenge. http://hdmilab.cn/ichallenge },
  url    = { http://hdmilab.cn/ichallenge },
}

Plain text

HDMILab iChallenge. http://hdmilab.cn/ichallenge

Source-stated terms

• Raw source string: Non-commercial research (challenge-specific)
• Normalized category: research-only
• Apparent scope: challenge_participation
• 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)