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OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods. / Kulyabin, Mikhail; Zhdanov, Aleksei; Nikiforova, Anastasia и др.
в: Scientific Data, Том 11, № 1, 365, 2024.

Результаты исследований: Вклад в журнал › Статья › Рецензирование

Harvard

Kulyabin, M, Zhdanov, A, Nikiforova, A, Stepichev, A, Kuznetsova, A, Ronkin, M, Borisov, V, Bogachev, A, Korotkich, S, Constable, P & Maier, A 2024, 'OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods', Scientific Data, Том. 11, № 1, 365. https://doi.org/10.1038/s41597-024-03182-7

APA

Kulyabin, M., Zhdanov, A., Nikiforova, A., Stepichev, A., Kuznetsova, A., Ronkin, M., Borisov, V., Bogachev, A., Korotkich, S., Constable, P., & Maier, A. (2024). OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods. Scientific Data, 11(1), [365]. https://doi.org/10.1038/s41597-024-03182-7

Vancouver

Kulyabin M, Zhdanov A, Nikiforova A, Stepichev A, Kuznetsova A, Ronkin M и др. OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods. Scientific Data. 2024;11(1):365. doi: 10.1038/s41597-024-03182-7

Author

Kulyabin, Mikhail ; Zhdanov, Aleksei ; Nikiforova, Anastasia и др. / OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods. в: Scientific Data. 2024 ; Том 11, № 1.

BibTeX

@article{77a2de5f415d40e0ac95f17056c95d1d,
title = "OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods",
abstract = "Optical coherence tomography (OCT) is a non-invasive imaging technique with 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. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset. {\textcopyright} The Author(s) 2024.",
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 Constable and Andreas Maier",
year = "2024",
doi = "10.1038/s41597-024-03182-7",
language = "English",
volume = "11",
journal = "Scientific Data",
issn = "2052-4463",
publisher = "Springer",
number = "1",

}

RIS

TY - JOUR

T1 - OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods

AU - Kulyabin, Mikhail

AU - Zhdanov, Aleksei

AU - Nikiforova, Anastasia

AU - Stepichev, Andrey

AU - Kuznetsova, Anna

AU - Ronkin, Mikhail

AU - Borisov, Vasilii

AU - Bogachev, Alexander

AU - Korotkich, Sergey

AU - Constable, Paul

AU - Maier, Andreas

PY - 2024

Y1 - 2024

N2 - Optical coherence tomography (OCT) is a non-invasive imaging technique with 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. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset. © The Author(s) 2024.

AB - Optical coherence tomography (OCT) is a non-invasive imaging technique with 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. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset. © The Author(s) 2024.

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U2 - 10.1038/s41597-024-03182-7

DO - 10.1038/s41597-024-03182-7

M3 - Article

VL - 11

JO - Scientific Data

JF - Scientific Data

SN - 2052-4463

IS - 1

M1 - 365

ER -

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