Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation

Gonzalez, Camila; Gotkowski, Karol; Bucher, Andreas; Fischbach, Ricarda; Kaltenborn, Isabel; Mukhopadhyay, Anirban

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.