MosMedData: COVID-19疫情期间进行的1110 次胸部CT扫描数据集
- 作者: Morozov S.1, Andreychenko A.1, Blokhin I.1, Gelezhe P.1, Gonchar A.1, Nikolaev A.1, Pavlov N.1, Chernina V.1, Gombolevskiy V.1
-
隶属关系:
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow
- 期: 卷 1, 编号 1 (2020)
- 页面: 49-59
- 栏目: 数据集
- URL: https://journals.rcsi.science/DD/article/view/46826
- DOI: https://doi.org/10.17816/DD46826
- ID: 46826
如何引用文章
详细
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, MoscowAnna 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, MoscowIvan 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, MoscowPavel 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, MoscowAnna 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, MoscowAlexander 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, MoscowNikolay 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, MoscowValeria 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, MoscowVictor 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
- 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
- Handbook of COVID-19 Prevention and Treatment. Ed. by T. Liang. Zhejiang University School of Medicine; 2020. 68 p.
- 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
- 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
- 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
- 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
- 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
- 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/
- 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.
- 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.
- 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
- 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
![](/img/style/loading.gif)