An exploration of uncertainty information for segmentation quality assessment

Including uncertainty information in the assessment of a segmentation of pathologic structures on medical images, offers the potential to increase trust into deep learning algorithms for the analysis of medical imaging. Here, we examine options to extract uncertainty information from deep learning segmentation models and the influence of the choice of cost functions on these uncertainty measures. To this end we train conventional UNets without...