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 1600 high-resolution OCT images labeled according to disease group and retinal pathology.

The dataset consists of the following categories and images:

- Age-Related Macular Degeneration - 885 images;

- Diabetic Macular Edema - 143 images;

- Epiretinal Membrane- 133 images;

- Normal - 284 images;

- Retinal Artery Occlusion - 22 images;

- Retinal Vein Occlusion - 93 images;

- Vitreomacular Interface Disease - 58 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. 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}}