Image Quality Assessments

Abstract

Deep learning with Convolutional Neural Networks (CNN) requires large number of training and test data sets which involves usually time-consuming visual inspection of medical image data. Recently, crowdsourcing methods have been proposed to gain such large training sets from untrained observers. In this paper, we propose to establish a lightweight method within the daily routine of radiologists in order to collect simple image quality annotations on a large scale. In multiple diagnostic centres, we analyse the acceptance rate of the radiologists and whether a substantial total number of professional annotations can be acquired to be used for deep learning later. Using a simple control panel with three buttons, 6 radiologists in 5 imaging centres assessed the image quality within their daily routine. Altogether, 1527 DICOM image studies (MR, CT, and X-ray) have been subjectively assessed in the first 70 days which demonstrates that a considerable number of training data sets can be collected with such a method in short time. The acceptance rate of the radiologists indicates that more data sets could be acquired if corresponding incentives are introduced as discussed in the paper. Since the proposed method is incorporated in the daily routine of radiologists, it can be easily scaled to even more number of professional observers.