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Uncertainty estimates of modern neuronal networks provide additionalinformation next to the computed predictions and are thus expected to improvethe understanding of the underlying model. Reliable uncertainties areparticularly interesting for safety-critical computer-assisted applications inmedicine, e.g., neurosurgical interventions and radiotherapy planning. Wepropose an uncertainty-driven sanity check for the identification ofsegmentation results that need particular expert review. Our method uses afully-convolutional neural network and computes uncertainty estimates by theprinciple of Monte Carlo dropout. We evaluate the performance of the proposedmethod on a clinical dataset with 30 postoperative brain tumor images. Themethod can segment the highly inhomogeneous resection cavities accurately (Dicecoefficients 0.792 $\pm$ 0.154). Furthermore, the proposed sanity check is ableto detect the worst segmentation and three out of the four outliers. Theresults highlight the potential of using the additional information from themodel's parameter uncertainty to validate the segmentation performance of adeep learning model.

Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation