Application of Active Learning-based on Uncertainty Quantification to Breast Segmentation in MRI

In medical image segmentation with deep learning, large amounts of annotated data are needed to train precise models. Such annotations are timeconsuming and costly to create, since medical experts need to ensure their quality. Active learning techniques may reduce the expert effort. In this work, we compare different sample selection strategies for training a model for breast segmentation in MR images using 3D U-Nets: We evaluate two uncertainty-based approaches that compute the voxel-wise entropy or epistemic uncertainty based on a Bayesian neural network approximated via Monte Carlo dropout and compare them against a random selection as baseline. We find that both uncertainty-based approaches improve over the baseline in the earlier iterations, but lead to a similar performance in the long run. In early iterations they are 2-4 active learning iterations ahead of the "random selection" strategy, which corresponds to one or several days of saved annotation time.We also assess how well the different uncertainty measures correlate with the segmentation quality and find that epistemic uncertainty is a better surrogate measure than the commonly used plain entropy.