COVID-19 Medical Imaging Dataset and Research Progress

doi:10.3778/j.issn.1002-8331.2106-0118

Abstract

Abstract:

As imaging technology has been playing an important role in the diagnosis and evaluation of the new coronavirus（COVID-19）, COVID-19 related datasets have been successively published. But few review articles discuss COVID-19 image processing, especially in datasets. To this end, the new coronary pneumonia datasets and deep learning models are sorted and analyzed, through COVID-19-related journal papers, reports, and related open-source dataset websites, which include Computer Tomography（CT） image and X-rays（CXR）image datasets. At the same time, the characteristics of the medical images presented by these datasets are analyzed. This paper focuses on collating and describing open-source datasets related to COVID-19 medical imaging. In addition, some important segmentation and classification models that perform well on the related datasets are analyzed and compared. Finally, this paper discusses the future development trend on lung imaging technology.

Key words: COVID-19 dataset, deep learning, image segmentation, image classification

摘要：

由于影像学技术在新型冠状病毒肺炎（COVID-19）的诊断和评估中发挥了重要作用，COVID-19相关数据集陆续被公布，但目前针对相关文献中数据集以及研究进展的整理相对较少。为此，通过COVID-19相关的期刊论文、报告和相关开源数据集网站，对涉及到的新冠肺炎数据集及深度学习模型进行整理和分析，包括计算机断层扫描（CT）图像数据集和X射线（CXR）图像数据集。对这些数据集呈现的医学影像的特征进行分析；重点论述开源数据集，以及在相关数据集上表现较好的分类和分割模型。最后讨论了肺部影像学技术未来的发展趋势。

关键词: COVID-19数据集, 深度学习, 图像分割, 图像分类

LIU Rui, DING Hui, SHANG Yuanyuan, SHAO Zhuhong, LIU Tie. COVID-19 Medical Imaging Dataset and Research Progress[J]. Computer Engineering and Applications, 2021, 57(22): 15-27.

刘锐，丁辉，尚媛园，邵珠宏，刘铁. COVID-19医学影像数据集及研究进展[J]. 计算机工程与应用, 2021, 57(22): 15-27.

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