MosMedData: COVID-19疫情期间进行的1110 次胸部CT扫描数据集

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在COVID-19大流行和雪崩式增加肺部计算机断层扫描的数量背景下,图像分析过程的自动化方法特别重要,使用这种方法将提高生产率并减少错误。高质量数据集的创建是人工智能技术发展的必要条件。人工智能算法对COVID-19的诊断具有足够的准确性。该数据集1包含有COVID-19征象的患者的匿名肺部CT图像和正常的胸部检查。一些研究使用感兴趣区域的二元像素遮罩进行标记(例如,肺结节整合和磨砂玻璃结节)。获取2020年3月1日至2020年4月25日期间的CT数据,提供给莫斯科市医院(俄罗斯)2。建议的数据集由Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported授权(CC BY-NC-ND 3.0)。

作者简介

Sergey Morozov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow

Email: morozov@npcmr.ru
ORCID iD: 0000-0001-6545-6170
SPIN 代码: 8542-1720

MD, PhD, Professor

俄罗斯联邦, Moscow

Anna Andreychenko

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow

Email: a.andreychenko@npcmr.ru
ORCID iD: 0000-0001-6359-0763
SPIN 代码: 6625-4186

MD

俄罗斯联邦, Moscow

Ivan Blokhin

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow

Email: i.blokhin@npcmr.ru
ORCID iD: 0000-0002-2681-9378
SPIN 代码: 3306-1387

MD

俄罗斯联邦, Moscow

Pavel Gelezhe

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow

Email: gelezhe.pavel@gmail.com
ORCID iD: 0000-0003-1072-2202
SPIN 代码: 4841-3234

MD, PhD

俄罗斯联邦, Moscow

Anna Gonchar

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow

Email: a.gonchar@npcmr.ru
ORCID iD: 0000-0001-5161-6540
SPIN 代码: 3513-9531

MD

俄罗斯联邦, Moscow

Alexander Nikolaev

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow

Email: a.e.nikolaev@yandex.ru
ORCID iD: 0000-0001-5151-4579
SPIN 代码: 1320-1651

MD

俄罗斯联邦, Moscow

Nikolay Pavlov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow

Email: n.pavlov@npcmr.ru
ORCID iD: 0000-0002-4309-1868
SPIN 代码: 9960-4160

MD, MPA

俄罗斯联邦, Moscow

Valeria Chernina

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow

Email: v.chernina@npcmr.ru
ORCID iD: 0000-0002-0302-293X
SPIN 代码: 8896-8051

MD

俄罗斯联邦, Moscow

Victor Gombolevskiy

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow

编辑信件的主要联系方式.
Email: g_victor@mail.ru
ORCID iD: 0000-0003-1816-1315
SPIN 代码: 6810-3279

MD, PhD, MPH

俄罗斯联邦, Moscow

参考

  1. Ai T, Yang Z, Hou H, et al. Correlation of chest CT and RT-PCR testing in Coronavirus Disease 2019 (COVID19) in China: a report of 1014 cases. Radiology. 2020;296(2):E32–E40. doi: 10.1148/radiol.2020200642
  2. Handbook of COVID-19 Prevention and Treatment. Ed. by T. Liang. Zhejiang University School of Medicine; 2020. 68 p.
  3. Huang Z, Zhao S, Li Z, et al. The battle against Coronavirus Disease 2019 (COVID-19): emergency management and infection control in a Radiology Department. J Am Coll Radiol. 2020;17(6):710–716. doi: 10.1016/j.jacr.2020.03.011
  4. Morozov SP, Gombolevskiy VA, Cherninа VY, et al. Prediction of lethal outcomes in COVID-19 cases based on the results chest computed tomography. Tuberculosis and Lung Diseases. 2020;98(6):7–14. (In Russ.) doi: 10.21292/2075-1230-2020-98-6-7-14
  5. Morozov S, Guseva E, Ledikhova N, et al. Telemedicine-based system for quality management and peer review in radiology. Insights Imaging. 2018;9(3):337–341. doi: 10.1007/s13244-018-0629-y
  6. Li L, Qin L, Xu Z, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology. 2020;296(2):E65–E71. doi: 10.1148/radiol.2020200905
  7. Ucar F, Korkmaz D. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses. 2020;140:109761. doi: 10.1016/j.mehy.2020.109761
  8. Vremennye metodicheskie rekomendatsii “Profilaktika, diagnostika i lechenie novoi koronavirusnoi infektsii (COVID-19). Versiya 9” (utv. Ministerstvom zdravookhraneniya RF 26 oktyabrya 2020). Available from: https://base.garant.ru/74810808/
  9. Morozov SP, Protsenko DN, Smetanina SV, editors. Radiation diagnostics of coronavirus disease (COVID-19): organization, methodology, interpretation of results: guidelines. Series “Best practices of radiation and instrumental diagnostics”. Issue 65. Moscow; 2020.
  10. Morozov SP, Vladzymyrskyy AV, Klyashtornyy VG, et al. Clinical acceptance of software based on artificial intelligence technologies (radiology). Series “Best practices in medical imaging”. Moscow; 2019. Issue 57.
  11. Cohen JP, Morrison P, Dao L. COVID-19 Image Data Collection [Internet]. 2020 [cited 2020 Mar 25]. Available from: https://arxiv.org/abs/2003.11597
  12. Jun M, Cheng G, Yixin W, et al. COVID-19 CT lung and infection segmentation dataset. Verson 1.0. 2020. doi: 10.5281/zenodo.3757476

补充文件

附件文件
动作
1. JATS XML
2. 图 1数据集的形成顺序。 注:CT—计算机断层摄影

下载 (212KB)
3. 图 2不同严重程度COVID-19患者胸部器官计算机断层标记示例 注:上一行,从左至右:COVID-19患者的CT轴向切片,从轻度(CT-1)到极重度(CT-4)。从左到右,下一行:打标后相同的CT数据。

下载 (304KB)
4. 图 3数据集中的数据存储结构。

下载 (350KB)

版权所有 © Morozov S., Andreychenko A., Blokhin I., Gelezhe P., Gonchar A., Nikolaev A., Pavlov N., Chernina V., Gombolevskiy V., 2020

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。

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