In this work, we examine the effectiveness of an uncertainty quantification framework known as Evidential Deep Learning applied in the context of biomedical image segmentation. This class of models involves assigning Dirichlet distributions as priors for segmentation labels, and enables a few distinct definitions of model uncertainties. Using the cardiac and prostate MRI images available in the Medical Segmentation Decathlon for validation, we found that Evidential Deep Learning models with U-Net backbones generally yielded superior correlations between prediction errors and uncertainties relative to the conventional baseline equipped with Shannon entropy measure, Monte-Carlo Dropout and Deep Ensemble methods. We also examined these models' effectiveness in active learning, finding that relative to the standard Shannon entropy-based sampling, they yielded higher point-biserial uncertainty-error correlations while attaining similar performances in Dice-Sorensen coefficients. These superior features of EDL models render them well-suited for segmentation tasks that warrant a critical sensitivity in detecting large model errors.

翻译：本研究探讨了证据深度学习这一不确定性量化框架在生物医学图像分割任务中的有效性。此类模型通过为分割标签分配狄利克雷分布作为先验，能够提供多种不同的模型不确定性定义。基于医学分割十项全能挑战赛提供的心脏与前列腺MRI图像进行验证，我们发现：相较于配备香农熵度量、蒙特卡洛丢弃法和深度集成方法的传统基线模型，采用U-Net骨干网络的证据深度学习模型普遍能实现预测误差与不确定性之间更优的关联性。我们进一步检验了这些模型在主动学习中的效能，发现相对于标准的基于香农熵的采样方法，该模型在保持相似戴斯-索伦森系数的同时，能获得更高的点二列不确定度-误差相关性。证据深度学习模型的这些优越特性使其特别适用于需要对重大模型误差保持高度敏感性的分割任务。