GOALS — Glaucoma OCT Layer Segmentation (MICCAI 2022)

300 circumpapillary OCT images. RNFL/GCIPL/choroid layer segmentation plus binary glaucoma classification.

At a glance

Field	Value
Short name	goals
Full name	GOALS — Glaucoma OCT Layer Segmentation (MICCAI 2022)
Primary category	oct
Contained modalities	oct
Tasks	segmentation, classification
Samples	300
Classes	3 (RNFL, GCIPL, Choroid)
Splits	train, test
Size	0.5 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	anonymous_direct
Route backend	Google Drive
Availability	available (checked 2026-07-21)
Acquisition support	loader_implemented_not_live_tested
Legacy sample-loader status	Standard loader included

Notes

Primary challenge page: https://aistudio.baidu.com/competition/detail/783/0/introduction. Automated downloader still tries the known Google Drive mirror (https://drive.google.com/file/d/1P1cLm9_Pwq4fum4lB1-LGUayO-NMvF5I/view) and then Zenodo record 6362363.

Access preflight and acquisition

CLI

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

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

Python

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

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

Upstream page: aistudio.baidu.com/competition

Source-term evidence: aistudio.baidu.com/competition

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

Citation

BibTeX

@misc{goals,
  title  = { GOALS — Glaucoma OCT Layer Segmentation (MICCAI 2022) },
  note   = { Fang H. et al., 'GOALS Challenge: A Large-Scale OCT Image Dataset for Glaucoma Analysis', MICCAI 2022 Workshop },
  year   = { 2022 },
  url    = { https://aistudio.baidu.com/competition/detail/783/0/introduction },
}

Plain text

Fang H. et al., 'GOALS Challenge: A Large-Scale OCT Image Dataset for Glaucoma Analysis', MICCAI 2022 Workshop.

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.

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• mario: MARIO: AMD-Progression Longitudinal OCT (MICCAI 2024) (30,000 records, cc-by)
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