虽然近年来在脑瘤分割的准确性方面取得了一些进展，但结果仍然存在着可靠性和稳健性较低的问题。不确定性估计是解决这个问题的有效方法，它提供了对分割结果的信心度量。目前基于分位数回归、贝叶斯神经网络、集成和蒙特卡洛丢弃等方法的不确定性估计方法在计算成本和一致性方面存在局限性。为了克服这些挑战，我们提出了一个基于Evidential Deep Learning (EDL) 的基于区域的分割框架，该框架可以生成可靠的不确定性地图和准确的分割结果，对噪声和图像损坏具有鲁棒性。我们使用证据理论将神经网络的输出解释为从输入特征中收集到的证据值。根据主观逻辑，证据被参数化为一个Dirichlet分布，而预测的概率被视为主观意见。为了评估我们的模型在分割和不确定性估计方面的性能，我们在BraTS 2020数据集上进行了定量和定性实验。结果表明我们提出的方法在量化分割不确定性和鲁棒分割肿瘤方面具有最佳性能。此外，我们提出的新框架保持了低计算成本和易于实现的优点，并显示了在临床应用方面的潜力。© 2022 作者。

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 and showed inferior segmentation results. In this paper, we proposed a region-based EDL segmentation framework that can generate reliable uncertainty maps and accurate segmentation results, which is robust to noise and image corruption. 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.© The Author(s) 2022.