GAMMA — Glaucoma grading from Multi-Modality imAges (Challenge dataset)

Image: Dataset Samples.

Short description

GAMMA is the first public multi-modality glaucoma grading dataset that pairs 2D color fundus photographs with 3D OCT volumes for each sample. It was released as part of the GAMMA challenge (OMIA8 / MICCAI 2021) to encourage algorithms that combine fundus and OCT information for automatic glaucoma grading.

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What the dataset contains

• Paired modalities: one macula/optic-disc centered 2D color fundus image and one 3D OCT volume (macula-centered) per sample.
• Samples: 300 paired samples (fundus + OCT) corresponding to 276 patients.
• Labeling / ground truth: each sample has a glaucoma grade (normal / early / progressive), derived from visual field mean deviation (MD) criteria; auxiliary labels include optic disc & cup (OD/OC) segmentation masks and fovea coordinates on the fundus images.
• Demographics: 276 Chinese patients, age range 19–77, mean ≈ 40.6 years; female ≈ 42%.
• Balanced classes: glaucoma ~50% of samples; within glaucoma: ~52% early, ~29% intermediate, ~19% advanced (intermediate+advanced grouped as “progressive” in challenge tasks).
• Acquisition devices: OCT volumes acquired using Topcon DRI OCT Triton; fundus images captured by KOWA and Topcon TRC-NW400 cameras (macula or midpoint between disc and macula).
• OCT spec: 3×3 mm en-face FOV; each volume contains 256 B-scans (cross-sectional frames).
• Image quality: manually checked; dataset split into three challenge sets (training, preliminary, final) with ~100 pairs per set.
• License / access: publicly available via the GAMMA grand-challenge page; dataset distributed under CC BY-NC-ND (Attribution-NonCommercial-NoDerivs).
• Official dataset page / access: https://gamma.grand-challenge.org/

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Intended tasks

Primary:

• Glaucoma grading from paired fundus + OCT (predict: normal / early-glaucoma / progressive-glaucoma).

Auxiliary:

• OD/OC segmentation (optic disc and optic cup masks on fundus images).
• Fovea localization (x,y coordinates).

Researchers may optionally use the auxiliary tasks to boost the main grading performance.

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Dataset structure (typical)

GAMMA/
├── images/
│ ├── fundus/ # fundus images (JPEG/PNG)
│ │ ├── sample_0001_fundus.jpg
│ │ └── ...
│ └── oct/ # OCT volumes (folder or volume files per sample)
│ ├── sample_0001_oct/ # 256 B-scans or a volume file (format described in README_original)
│ └── ...
├── labels/
│ ├── grades.csv # sample_id, grade (normal/early/progressive), MD values, other clinical metadata
│ ├── fovea_coords.csv # sample_id, x, y
│ └── od_oc_masks/ # per-sample masks (optional; may be in separate archive)
│ ├── sample_0001_od.png
│ └── ...
└── README_original.txt

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How samples were graded

Glaucoma grading ground truth was determined using visual field mean deviation (MD) thresholds from visual field tests performed the same day as OCT:

• Early: MD > −6 dB
• Intermediate: −12 dB < MD ≤ −6 dB
• Advanced: MD ≤ −12 dB
  For the main challenge, intermediate + advanced were grouped as progressive-glaucoma.

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Size & splits

• Total paired samples: 300 (fundus + OCT)
• Patients: 276 (some bilateral samples)
• Class distribution: ~50% glaucoma / 50% non-glaucoma; within glaucoma: early ≈ 52%, intermediate ≈ 28.7%, advanced ≈ 19.3%
• Challenge splits: approximately 100 pairs for training, 100 for preliminary, 100 for final test (samples from each category distributed across splits).

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Recommended uses & notes

• Use paired modalities (fundus + OCT) for multimodal fusion models — combining morphological cues (fundus OD/OC, vCDR) and structural OCT features (RNFL thickness) improves grading.
• Auxiliary tasks (OD/OC masks, fovea) are provided to support explainability and localized feature extraction.
• Respect the CC BY-NC-ND license for redistribution and commercial restrictions.

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Citation / sources

Please cite the GAMMA challenge paper and dataset when using the data:

• Wu J., Fang H., Li F., Fu H., Lin F., et al., “GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.” (paper / challenge summary). arXiv:2202.06511; journal: Medical Image Analysis (2023). DOI: 10.1016/j.media.2023.102938.
• Official dataset page (host & download): https://gamma.grand-challenge.org/

Primary references used to prepare this README:

• arXiv / GAMMA challenge paper: https://arxiv.org/abs/2202.06511
• Final journal version / PubMed entry: https://pubmed.ncbi.nlm.nih.gov/37806020/
• GAMMA challenge (Grand Challenge) dataset page: https://gamma.grand-challenge.org/

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