  Automatic segmentation of lung lesions in computer tomography has the
potential to ease the burden of clinicians during the Covid-19 pandemic. Yet
predictive deep learning models are not trusted in the clinical routine due to
failing silently in out-of-distribution (OOD) data. We propose a lightweight
OOD detection method that exploits the Mahalanobis distance in the feature
space. The proposed approach can be seamlessly integrated into state-of-the-art
segmentation pipelines without requiring changes in model architecture or
training procedure, and can therefore be used to assess the suitability of
pre-trained models to new data. We validate our method with a patch-based
nnU-Net architecture trained with a multi-institutional dataset and find that
it effectively detects samples that the model segments incorrectly.
