A Pareto front based methodology to better assess medical image registration algorithms
Samuel Joutard, Reuben Dorent, Tom K. Vercauteren, Marc Modat · Medical Imaging 2022: Image Processing · 2022
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.