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

Samuel Joutard; Reuben Dorent; Tom Vercauteren; Marc Modat

doi:10.1117/12.2611081

4 April 2022 A Pareto front based methodology to better assess medical image registration algorithms

Samuel Joutard, Reuben Dorent, Tom Vercauteren, Marc Modat

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120322R (2022) https://doi.org/10.1117/12.2611081
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

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.

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Citation Download Citation

Samuel Joutard, Reuben Dorent, Tom Vercauteren, and Marc Modat "A Pareto front based methodology to better assess medical image registration algorithms", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120322R (4 April 2022); https://doi.org/10.1117/12.2611081

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KEYWORDS

Image registration

Medical imaging

Chest

Computed tomography

Optimization (mathematics)

Transform theory

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