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)
First published	Unknown
Publication date precision	Unknown
Publication date evidence	Unknown
Publication date source field	Unknown
Publication date reviewed	Unknown
Primary category	oct
Resource role	collection
Dataset family	ichallenge_gamma
Contained modalities	oct
Tasks	segmentation, classification
Primary reported quantity	300 volumes
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

Reported quantities

Role	Count	Unit	Scope	Basis	Evidence
Primary	300	volumes	Primary quantity reported in the reviewed catalog source	legacy_catalog_field	hdmilab.cn/ichallenge

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

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/.

Dataset family

This record belongs to ichallenge_gamma. Family links group documented collection/component records or exact task views; they do not imply independent cohorts.

• gamma: GAMMA — Glaucoma Grading from Multi-Modality Images (component_dataset)

Documented relationships

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

• gamma is component of this record: GAMMA is one of the named resources exposed through the cataloged iChallenge OCT portal record. (evidence)
• This record is has component gamma: The cataloged iChallenge OCT portal record explicitly includes the GAMMA multimodal challenge resource. (evidence)

Access information and download

CLI

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

# Download, 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 download

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.

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