Uncertainty Aware Segmentation Quality Assessment in Medical Images

Image segmentation is a fundamental step in most computational biomedical image analysis pipelines. During model training and validation, we can measure segmentation performance using well-established similarity metrics like the Dice coefficient. However, once the model is deployed in a clinical scenario, this is no longer possible as manual annotations are not available. In addition, segmentation models that produce a solution with no indication of its reliability result in harder adoption by end-users. To approach these two challenges, this paper introduces a segmentation quality prediction framework that does not rely on manual annotations in test time. This framework integrates uncertainty estimates on the underlying segmentation model, which we show to be advantageous for quality scoring purposes. We validate our approach on a popular skin lesion segmentation dataset, carefully analyzing the impact of different uncertainty modeling and estimation techniques on the performance of segmentation quality prediction performance.