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

Citation Author(s):
Mikhail
Kulyabin
University of Erlangen–Nuremberg
Aleksei
Zhdanov
Ural Federal University named after the first President of Russia B. N. Yeltsin
Anastasia
Nikiforova
Ophthalmosurgery Clinic "Professorskaya Plus"
Andrey
Stepichev
Ophthalmosurgery Clinic "Professorskaya Plus"
Anna
Kuznetsova
Ophthalmosurgery Clinic "Professorskaya Plus"
Vasilii
Borisov
Ural Federal University named after the first President of Russia B. N. Yeltsin
Mikhail
Ronkin
Ural Federal University named after the first President of Russia B. N. Yeltsin
Alexander
Bogachev
Ophthalmosurgery Clinic "Professorskaya Plus"
Sergey
Korotkich
Ophthalmosurgery Clinic "Professorskaya Plus"
Submitted by:
Mikhail Kulyabin
Last updated:
Mon, 04/15/2024 - 04:28
DOI:
10.21227/fpvs-8n55
Data Format:
*.jpg (zip)
Research Article Link:
OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods
License:
Creative Commons Attribution
2666 Views
Categories:
Artificial Intelligence
Image Processing
Biomedical and Health Sciences
Computer Vision
Keywords:
OCT, Optical Coherence Tomography
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Abstract 

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) dataset comprising over 2000 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 - 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.

Instructions: 

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

@article{kulyabin2024octdl,
  title={OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods},
  author={Kulyabin, Mikhail and Zhdanov, Aleksei and Nikiforova, Anastasia and Stepichev, Andrey 
          and Kuznetsova, Anna and Ronkin, Mikhail and Borisov, Vasilii and Bogachev, Alexander 
          and Korotkich, Sergey and Constable, Paul A and Maier, Andreas},
  journal={Scientific Data},
  volume={11},
  number={1},
  pages={365},
  year={2024},
  publisher={Nature Publishing Group UK London},
  doi={https://doi.org/10.1038/s41597-024-03182-7}
}

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