Metric Learning for Image Registration

Roland Kwitt, Marc Niethammer, Francois-Xavier Vialard

Research output: Chapter in Book/Report/Conference proceeding/Legal commentaryConference contributionResearchpeer-review


Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.
Original languageEnglish
Title of host publicationProceedings of the 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Number of pages9
Publication statusPublished - 2019
EventIEEE/CVF Conference on Computer Vision and Pattern Recognition - Long Beach Convention Center, Long Beach, CA, United States
Duration: 16 Jun 201920 Jun 2019


ConferenceIEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR
CountryUnited States
CityLong Beach, CA


  • Image registration
  • Deep learning

Fields of Science and Technology Classification 2012

  • 102 Computer Sciences

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