Automatic Quality Control for Population Imaging: A Generic Unsupervised Approach

Population imaging studies have opened new opportunities for a comprehensive characterization of disease phenotypes by providing large-scale databases. A major challenge is related to the ability to amass automatically and accurately the vast amount of data and hence to develop streamlined image analysis pipelines that are robust to the varying image quality. This requires a generic and fully unsupervised quality assessment technique. However, existing methods are designed for specific types of artefacts and cannot detect incidental unforeseen artefacts. Furthermore, they require manual annotations, which is a demanding task, prone to error, and in some cases ambiguous. In this study, we propose a generic unsupervised approach to simultaneously detect and localize the artefacts. We learn the normal image properties from a large dataset by introducing a new image representation approach based on an optimal coverage of images with the learned visual dictionary. The artefacts are then detected and localized as outliers. We tested our method on a femoral DXA dataset with 1300 scans. The sensitivity and specificity are 81.82 % and 94.12 %, respectively.