Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland.
Department of Computer Graphics, Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland.
Sci Data. 2023 Jun 2;10(1):348. doi: 10.1038/s41597-023-02229-5.
The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions.
SARS-CoV-2 大流行的爆发使全球医疗体系不堪重负，导致诊断和所需医疗援助的等待时间延长。胸部 X 光片（CXR）是最常见的 COVID-19 诊断方法之一，因此已经开发出许多基于图像的 COVID-19 检测人工智能工具，这些工具通常是在少数 COVID-19 阳性患者的图像上进行训练的。因此，需要高质量和标注良好的 CXR 图像数据库。本文介绍了 POLCOVID 数据集，该数据集包含来自波兰 15 家医院的 COVID-19 或其他类型肺炎患者以及健康个体的胸部 X 光（CXR）图像。原始射线照片附有预处理后的仅包含肺部区域的图像以及使用分割模型获得的相应肺部蒙版。此外，还为 POLCOVID 数据集的一部分和另外四个公开可用的 CXR 图像集合提供了手动创建的肺部蒙版。POLCOVID 数据集可用于肺炎或 COVID-19 的诊断，而匹配的图像和肺部蒙版集可用于开发肺部分割解决方案。
