  Despite recent advances in the accuracy of brain tumor segmentation, the
results still suffer from low reliability and robustness. Uncertainty
estimation is an efficient solution to this problem, as it provides a measure
of confidence in the segmentation results. The current uncertainty estimation
methods based on quantile regression, Bayesian neural network, ensemble, and
Monte Carlo dropout are limited by their high computational cost and
inconsistency. In order to overcome these challenges, Evidential Deep Learning
(EDL) was developed in recent work but primarily for natural image
classification. In this paper, we proposed a region-based EDL segmentation
framework that can generate reliable uncertainty maps and robust segmentation
results. We used the Theory of Evidence to interpret the output of a neural
network as evidence values gathered from input features. Following Subjective
Logic, evidence was parameterized as a Dirichlet distribution, and predicted
probabilities were treated as subjective opinions. To evaluate the performance
of our model on segmentation and uncertainty estimation, we conducted
quantitative and qualitative experiments on the BraTS 2020 dataset. The results
demonstrated the top performance of the proposed method in quantifying
segmentation uncertainty and robustly segmenting tumors. Furthermore, our
proposed new framework maintained the advantages of low computational cost and
easy implementation and showed the potential for clinical application.
