  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.
