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)
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	current_dataset
Dataset family	goals
Contained modalities	oct
Tasks	segmentation, classification
Primary reported quantity	300 images
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

Reported quantities

Role	Count	Unit	Scope	Basis	Evidence
Primary	300	images	Primary quantity reported in the reviewed catalog source	legacy_catalog_field	aistudio.baidu.com/competition

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

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.

Documented relationships

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

• mm_retinal_reason is derived from this record: The version-pinned official dataset card lists this record among the CFP or OCT sources used to construct MM-Retinal-Reason. (evidence)
• multieye is derived from this record: The MultiEYE paper names this record as one of the public fundus or OCT sources assembled for the benchmark. (evidence)

Access information and download

CLI

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

# Download, 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 download

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.

Similar resources by shared modality

• syn_oct: SYN-OCT Synthetic Glaucoma OCT Dataset (200,000 images, cc-by)
• multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (103,959 images, mit)
• eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 question answer pairs, unknown)
• kermany_oct: Kermany OCT 2018: Retinal OCT Image Classification (84,484 images, cc-by)
• lmod_plus: LMOD+ Multimodal Ophthalmology Benchmark (32,633 annotated instances, unknown)
• harvard_fairvision: Harvard-FairVision (AMD + DR + Glaucoma, paired SLO + OCT) (30,000 participants, cc-by-nc-nd)
• mario: MARIO: AMD-Progression Longitudinal OCT (MICCAI 2024) (30,000 images, cc-by)
• mmrdr: MMRDR: Multi-Modal Retinal Diabetic Retinopathy Dataset (24,460 images, cc-by)