Quality control (QC) of medical images is essential to ensure that downstream
analyses such as segmentation can be performed successfully. Currently, QC is
predominantly performed visually at significant time and operator cost. We aim
to automate the process by formulating a probabilistic network that estimates
uncertainty through a heteroscedastic noise model, hence providing a proxy
measure of task-specific image quality that is learnt directly from the data.
By augmenting the training data with different types of simulated k-space
artefacts, we propose a novel cascading CNN architecture based on a
student-teacher framework to decouple sources of uncertainty related to
different k-space augmentations in an entirely self-supervised manner. This
enables us to predict separate uncertainty quantities for the different types
of data degradation. While the uncertainty measures reflect the presence and
severity of image artefacts, the network also provides the segmentation
predictions given the quality of the data. We show models trained with
simulated artefacts provide informative measures of uncertainty on real-world
images and we validate our uncertainty predictions on problematic images
identified by human-raters