Zaffino Paolo, Marzullo Aldo, Moccia Sara, Calimeri Francesco, De Momi Elena, Bertucci Bernardo, Arcuri Pier Paolo, Spadea Maria Francesca
Department of Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, 88100 Catanzaro, Italy.
Department of Mathematics and Computer Science, University of Calabria, 87036 Rende, Italy.
Bioengineering (Basel). 2021 Feb 16;8(2):26. doi: 10.3390/bioengineering8020026.
The coronavirus disease 19 (COVID-19) pandemic is having a dramatic impact on society and healthcare systems. In this complex scenario, lung computerized tomography (CT) may play an important prognostic role. However, datasets released so far present limitations that hamper the development of tools for quantitative analysis. In this paper, we present an open-source lung CT dataset comprising information on 50 COVID-19-positive patients. The CT volumes are provided along with (i) an automatic threshold-based annotation obtained with a Gaussian mixture model (GMM) and (ii) a scoring provided by an expert radiologist. This score was found to significantly correlate with the presence of ground glass opacities and the consolidation found with GMM. The dataset is freely available in an ITK-based file format under the CC BY-NC 4.0 license. The code for GMM fitting is publicly available, as well. We believe that our dataset will provide a unique opportunity for researchers working in the field of medical image analysis, and hope that its release will lay the foundations for the successfully implementation of algorithms to support clinicians in facing the COVID-19 pandemic.
新型冠状病毒肺炎（COVID-19）大流行正在对社会和医疗系统产生巨大影响。在这种复杂的情况下，肺部计算机断层扫描（CT）可能发挥重要的预后作用。然而，迄今为止发布的数据集存在局限性，阻碍了定量分析工具的开发。在本文中，我们展示了一个开源肺部CT数据集，其中包含50名COVID-19阳性患者的信息。CT容积数据与（i）使用高斯混合模型（GMM）获得的基于自动阈值的标注以及（ii）由专业放射科医生提供的评分一同提供。发现该评分与磨玻璃影的存在以及GMM发现的实变显著相关。该数据集以基于ITK的文件格式在CC BY-NC 4.0许可下免费提供。用于GMM拟合的代码也已公开。我们相信，我们的数据集将为医学图像分析领域的研究人员提供一个独特的机会，并希望其发布将为成功实施支持临床医生应对COVID-19大流行的算法奠定基础。
