POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021)

Abstract

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.

Fig. 1

Exemplary images included in the POLCOVID dataset for one representative of each diagnosis group. Original CXR images (a), preprocessed lung area images (b), and lung masks (c) of normal, pneumonia and COVID-19 cases.

Fig. 2

Demographic summary of the cohort. Proportions of sexes in diagnosis groups and in total are accompanied by numbers of images and numbers of missing records (a). Age distributions in diagnosis groups and in total (b).

Fig. 3

Two-dimensional Gaussian mixture model (2D GMM) fitted on the results of nUMAP feature extraction for each diagnosis category. COVID-19 (a), other-type pneumonia (b), and normals (c).