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README.md

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+# 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/
+
+---