Understanding the uncertainty inherent in deep learning-based image
registration models has been an ongoing area of research. Existing methods have
been developed to quantify both transformation and appearance uncertainties
related to the registration process, elucidating areas where the model may
exhibit ambiguity regarding the generated deformation. However, our study
reveals that neither uncertainty effectively estimates the potential errors
when the registration model is used for label propagation. Here, we propose a
novel framework to concurrently estimate both the epistemic and aleatoric
segmentation uncertainties for image registration. To this end, we implement a
compact deep neural network (DNN) designed to transform the appearance
discrepancy in the warping into aleatoric segmentation uncertainty by
minimizing a negative log-likelihood loss function. Furthermore, we present
epistemic segmentation uncertainty within the label propagation process as the
entropy of the propagated labels. By introducing segmentation uncertainty along
with existing methods for estimating registration uncertainty, we offer vital
insights into the potential uncertainties at different stages of image
registration. We validated our proposed framework using publicly available
datasets, and the results prove that the segmentation uncertainties estimated
with the proposed method correlate well with errors in label propagation, all
while achieving superior registration performance.

我们提出了一个新的框架，同时估计图像配准的先验和其它分割不确定性，通过最小化负对数似然损失函数，将外观不一致性转化为其它分割不确定性，并通过引入分割不确定性以及现有的估计配准不确定性方法，在图像配准的不同阶段提供了重要的可视化结果。通过使用公开可用的数据集验证了我们提出的方法，并且结果证明了所提方法估计的分割不确定性与标签传播错误之间的良好关联性，同时实现了优越的配准性能。