| --- |
| language: |
| - en |
| license: other |
| tags: |
| - medical |
| - ophthalmology |
| - fundus-image |
| - oct-volume |
| - multi-modal |
| - image-classification |
| - image-segmentation |
| - glaucoma-grading |
| - optic-disc-segmentation |
| task_categories: |
| - image-classification |
| - image-segmentation |
| - object-detection |
| task_ids: |
| - multi-class-image-classification |
| - semantic-segmentation |
| pretty_name: GAMMA (Glaucoma grading from Multi-Modality imAges) Challenge Dataset |
| size_categories: |
| - 100<n<1K |
| annotations_creators: |
| - expert-generated |
| source_datasets: |
| - original |
| source_data_urls: |
| - https://gamma.grand-challenge.org/ |
| - https://arxiv.org/abs/2202.06511 |
| --- |
| |
| # GAMMA — Glaucoma grading from Multi-Modality imAges (Challenge dataset) |
|
|
| <table align="center"> |
| <tr> |
| <td width="100%" align="center"> |
| <img src="rm_images/Merged_Fundus_Images_with_Captions.jpg" alt="Merged Dataset Samples" style="max-width: 100%; height: auto;"> |
| <br> |
| <p><strong>Image:</strong> Dataset Samples.</p> |
| </td> |
| </tr> |
| </table> |
| |
| ## 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. |
|
|
| --- |
|
|
| ## 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/ |
|
|
| --- |
|
|
| ## 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. |
|
|
| --- |
|
|
| ## Dataset structure (typical) |
|
|
| ```text |
| 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 |
| ``` |
|
|
| --- |
|
|
| ## 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**. |
|
|
| --- |
|
|
| ## 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). |
|
|
| --- |
|
|
| ## 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. |
|
|
| --- |
|
|
| ## 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/ |
|
|
| --- |
