@inbook{58bee901ba524fc49eb4eda38f23e065,
title = "A Pareto front based methodology to better assess medical image registration algorithms",
abstract = "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.",
author = "Samuel Joutard and Reuben Dorent and Tom Vercauteren and Marc Modat",
year = "2022",
month = apr,
day = "4",
doi = "10.1117/12.2611081",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
editor = "Olivier Colliot and Ivana Isgum and Landman, {Bennett A.} and Loew, {Murray H.}",
booktitle = "Medical Imaging 2022",
}