On the Applicability of Registration Uncertainty

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11765)


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 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, \(U_\mathrm {t}\), and label uncertainty \(U_\mathrm {l}\). The conventional way of using \(U_\mathrm {t}\) to quantify \(U_\mathrm {l}\) 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.


Image registration Registration uncertainty 



MS was supported by the International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study. This work was also supported by NIH grants P41EB015898, P41EB015902 and 5R01NS049251.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Brigham and Women’s Hospital, Harvard Medical SchoolBostonUSA
  2. 2.Graduate School of Frontier SciencesThe University of TokyoTokyoJapan
  3. 3.School of ComputingQueen’s UniversityKingstonCanada
  4. 4.School of Engineering ScienceSimon Fraser UniversityBurnabyCanada
  5. 5.Computing Science DepartmentUniversity of AlbertaEdmontonCanada
  6. 6.McKelvey School of EngineeringWashington University in St. LouisSt. LouisUSA
  7. 7.Ecole de Technologie SuperieureMontrealCanada
  8. 8.Center for Advanced Intelligence ProjectRIKENTokyoJapan

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