Multimodal SLO-OCT ophthalmic imaging dataset
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<div class="dataset-summary">
<h2>Multimodal Ophthalmic Imaging Dataset</h2>
The code for our paper is at code.zip, code.z01, ..., code.z07.
The dataset is at datazet.zip, dataset.z01, ..., dataset.z205. The labels are at the labels folder.
<div class="modalities-section">
<h3>Modalities</h3>
<ul>
<li>2D SLO images (Scanning Laser Ophthalmoscopy)</li>
<li>3D OCT volumes:
<ul>
<li>Macular-centered OCT (OCT<sub>macular</sub>)</li>
<li>Disc-centered OCT (OCT<sub>disc</sub>)</li>
</ul>
</li>
</ul>
</div>
<div class="diseases-section">
<h3>Diseases Covered</h3>
<ul>
<li>Macular Edema (ME)</li>
<li>Diabetic Retinopathy (DR)</li>
<li>Glaucoma (GL)</li>
</ul>
</div>
<div class="composition-section">
<h3>Dataset Composition</h3>
<table border="1">
<tr>
<th>Modality Combinations</th>
<th># Samples</th>
</tr>
<tr><td>SLO (only) </td><td>1822</td></tr>,
<tr><td>OCT<sub>macular</sub> (only) </td><td>2092</td></tr>,
<tr><td>OCT<sub>disc</sub> (only) </td><td>2310</td></tr>,
<tr><td>SLO + OCT<sub>macular</sub> (paired)</td><td>1346</td></tr>,
<tr><td>SLO + OCT<sub>disc</sub> (paired)</td><td>1170</td></tr>,
<tr><td><strong>Total</strong></td><td><strong>8740</strong></td></tr>.
</table>
<p>Collected from 5235 patients (2019-2022) using:</p>
<ul>
<li>Optos Panoramic200 (SLO)</li>
<li>CIRRUS HD-OCT 500 (OCT)</li>
</ul>
</div>
<div class="features-section">
<h3>Key Features</h3>
<ul>
<li><strong>Real-world diversity:</strong>
<ul>
<li>Includes both modality-complete and modality-incomplete samples</li>
<li>Reflects clinical scenarios with personalized exams</li>
</ul>
</li>
<li><strong>Supports multiple diseases</strong> (ME, DR, GL) unlike single-disease datasets</li>
</ul>
</div>
<div class="labeling-section">
<h3>Labeling</h3>
<ul>
<li>Sources:
<ul>
<li>Electronic medical records</li>
<li>Ophthalmologist reviews</li>
</ul>
</li>
<li>Classification:
<ul>
<li>ME/DR: Binary (present/absent)</li>
<li>GL: Ternary (glaucoma/non-glaucoma/suspicious)</li>
</ul>
</li>
</ul>
</div>
<div class="splits-section">
<h3>Splits</h3>
<ul>
<li>Stratified 60:20:20 train/val/test split</li>
<li>Addresses class imbalance</li>
</ul>
</div>
</div>
# 多模态眼科成像数据集
本论文配套代码存放于`code.zip`、`code.z01`……`code.z07`等分卷压缩包中。数据集本体存放于`dataset.zip`、`dataset.z01`……`dataset.z205`等分卷压缩包中,标签文件置于`labels`文件夹内。
## 成像模态
- 2D扫描激光检眼镜(Scanning Laser Ophthalmoscopy,SLO)图像
- 3D光学相干断层扫描(Optical Coherence Tomography,OCT)容积数据:
- 黄斑中心光学相干断层扫描(OCT<sub>黄斑</sub>)
- 视盘中心光学相干断层扫描(OCT<sub>视盘</sub>)
## 覆盖疾病
- 黄斑水肿(Macular Edema,ME)
- 糖尿病视网膜病变(Diabetic Retinopathy,DR)
- 青光眼(Glaucoma,GL)
## 数据集构成
| 模态组合 | 样本数量 |
| ---- | ---- |
| 仅SLO | 1822 |
| 仅OCT<sub>黄斑</sub> | 2092 |
| 仅OCT<sub>视盘</sub> | 2310 |
| 配对SLO + OCT<sub>黄斑</sub> | 1346 |
| 配对SLO + OCT<sub>视盘</sub> | 1170 |
| **总计** | **8740** |
本数据集采集自2019年至2022年间的5235名患者,使用的成像设备包括:
- Optos Panoramic200(用于SLO成像)
- CIRRUS HD-OCT 500(用于OCT成像)
## 核心特性
- **真实场景多样性**:
- 涵盖模态完整与模态缺失两类样本
- 贴合包含个性化检查的临床实际场景
- **支持多疾病分类**:覆盖黄斑水肿、糖尿病视网膜病变、青光眼三类疾病,区别于单疾病数据集。
## 标注流程
- **标注来源**:
- 电子病历
- 眼科医师审阅结果
- **分类任务设定**:
- 黄斑水肿、糖尿病视网膜病变:二分类任务(存在/不存在)
- 青光眼:三分类任务(青光眼/非青光眼/疑似青光眼)
## 数据集划分
- 采用分层抽样的60:20:20训练集/验证集/测试集划分方案
- 该划分方式可有效缓解类别不平衡问题
提供机构:
Harvard Dataverse
创建时间:
2025-05-09



