On the applicability of registration uncertainty

Jax Luo

Abstract:

Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If surgeons receive inaccurately
calculated registration uncertainty and misplace unwarranted confidence
in the alignment solutions, severe consequences may result. For probabilistic image registration (PIR), the predominant way to quantify the
registration uncertainty is by using summary statistics of the distribution
of transformation parameters. The majority of existing research focuses
on trying out different summary statistics as well as means to exploit
them. Distinctively, in this paper, we study two rarely examined topics:
(1) whether those summary statistics of the transformation distribution
most informatively represent the registration uncertainty; (2) Does utilizing the registration uncertainty always be beneficial. We show that there are two types of uncertainties: the transformation uncertainty, Ut, and label uncertainty Ul. The conventional way of using Ut to quantify Ul is inappropriate and can be misleading. By a real data experiment, we also
share a potentially critical finding that making use of the registration
uncertainty may not always be an improvement.