Despite recent improvements in the accuracy of brain tumor segmentation, the results still exhibit low levels of confidence and robustness. Uncertainty estimation is one effective way to change this situation, as it provides a measure of confidence in the segmentation results. In this paper, we propose a trusted brain tumor segmentation network which can generate robust segmentation results and reliable uncertainty estimations without excessive computational burden and modification of the backbone network. In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the segmentation as a Dirichlet distribution. Meanwhile, the trusted segmentation framework learns the function that gathers reliable evidence from the feature leading to the final segmentation results. Overall, our unified trusted segmentation framework endows the model with reliability and robustness to out-of-distribution samples. To evaluate the effectiveness of our model in robustness and reliability, qualitative and quantitative experiments are conducted on the BraTS 2019 dataset.
翻译:尽管最近脑肿瘤分解的准确性有所改进,但结果仍显示信心和稳健度仍然较低。不确定估计是改变这种情况的有效方法之一,因为它提供了对分解结果的一种信任度。在本文中,我们提议建立一个可信任的脑肿瘤分解网络,可以产生稳健的分解结果和可靠的不确定性估计,而不会过度计算负担和修改主干网。在我们的方法中,不确定性是明确使用主观逻辑理论来模拟的,这种理论将主干神经网络的预测视为主观意见,将分解的等级概率参数作为分解分布的参数。与此同时,可信任的分解框架从最终分解结果的特征中收集可靠证据的功能。总体而言,我们统一的可信赖分解框架将模型可靠和稳健地归结出分配样本。为了评估我们模型在稳健性和可靠性方面的有效性,在BRATS 2019数据集上进行了定性和定量实验。