A Pareto front based methodology to better assess medical image registration algorithms

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Non-linear registration models optimize two conflicting objectives, a content-matching term and a deformation smoothness measure. As the desired smoothness regime is problem-specific, there is a need to better compare generic registration algorithms across different smoothness regimes. We propose to compare registration algorithms by estimating their content-matching vs deformation smoothness Pareto front. Specifically, we assess the deformation smoothness level reached by each algorithm at different content-matching levels. We introduce a new objective function to sample the Pareto front along a specific iso-content-matching line. We demonstrate the applicability of our method on chest-CT inter-patient registration by comparing 5 learning-based registration algorithms.
Original languageEnglish
Title of host publicationMedical Imaging 2022
Subtitle of host publicationImage Processing
EditorsOlivier Colliot, Ivana Isgum, Bennett A. Landman, Murray H. Loew
ISBN (Electronic)9781510649392
Publication statusPublished - 4 Apr 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


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