OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods

Optical coherence tomography (OCT) is a non-invasive imaging technique that has extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology.

The dataset consists of the following categories and images:
- Age-Related Macular Degeneration - 1231 images;
- Diabetic Macular Edema - 147 images;
- Epiretinal Membrane- 155 images;
- Normal - 332 images;
- Retinal Artery Occlusion - 22 images;
- Retinal Vein Occlusion - 101 images;
- Vitreomacular Interface Disease - 76 images.

This dataset is published to provide researchers and developers with access to a large set of labeled images, which contributes to the development and improvement of algorithms for the automatic processing and analysis of OCT images for early diagnosis and monitoring of eye diseases. CSV file consists of file_name, disease, subcategory, condition, patient_id, eye, sex, year, image_width, and image_height. The dataset will be updated periodically.

For more information and details about the dataset see: https://arxiv.org/abs/2312.08255

@misc{kulyabin2023octdl,
title={OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods},
author={Mikhail Kulyabin and Aleksei Zhdanov and Anastasia Nikiforova and Andrey Stepichev and Anna Kuznetsova and Mikhail Ronkin and Vasilii Borisov and Alexander Bogachev and Sergey Korotkich and Paul A Constable and Andreas Maier},
year={2023},
eprint={2312.08255},
archivePrefix={arXiv},
primaryClass={eess.IV}
}

光学相干断层扫描（OCT）是一种无创成像技术，在眼科领域具有广泛的应用。OCT能够可视化视网膜层，在视网膜疾病的早期检测和监测中扮演着至关重要的角色。OCT利用光波干涉原理，生成视网膜微结构的详细图像，使其成为诊断眼科状况的有价值工具。光学相干断层扫描图像深度学习方法数据集（OCTDL）包含超过2000张OCT图像，根据疾病组和视网膜病理学进行标注。

该数据集包含以下类别和图像：
- 年龄相关性黄斑变性 - 1231张图像；
- 糖尿病性黄斑水肿 - 147张图像；
- 脉络膜视网膜膜 - 155张图像；
- 正常 - 332张图像；
- 视网膜动脉阻塞 - 22张图像；
- 视网膜静脉阻塞 - 101张图像；
- 视网膜玻璃体交界病 - 76张图像。

该数据集的发布旨在为研究人员和开发者提供访问大量标注图像的机会，有助于促进自动处理和分析OCT图像算法的开发与优化，以实现眼科疾病的早期诊断和监测。CSV文件包含以下字段：文件名、疾病、亚类、状况、患者ID、眼睛、性别、年份、图像宽度和图像高度。数据集将定期更新。

更多关于数据集的信息和详情请参阅：https://arxiv.org/abs/2312.08255

@misc{kulyabin2023octdl,
title={OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods},
author={Mikhail Kulyabin and Aleksei Zhdanov and Anastasia Nikiforova and Andrey Stepichev and Anna Kuznetsova and Mikhail Ronkin and Vasilii Borisov and Alexander Bogachev and Sergey Korotkich and Paul A Constable and Andreas Maier},
year={2023},
eprint={2312.08255},
archivePrefix={arXiv},
primaryClass={eess.IV}
}