Multi-modal deep learning based 3D image registration for osseous anatomies

Master thesis (2022)

Authors

H.S. van Ulsen Electrical Engineering, Mathematics and Computer Science

Contributors

M. Staring Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
Marijn van Stralen University Medical Center Utrecht (supervisor 1)
M.J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
K.A. Hildebrandt Computer Graphics and Visualisation - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Hidde van Ulsen
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Published Date

14-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Image registration is a fundamental requirement for many medical applications. In recent years, deep learning approaches for registration have shown to be a promising alternative to conventional methods. However, most learning based methods do not consider the different physical properties of various tissues, which can result in unrealistic deformation in anatomical regions where both deformable tissue and rigid bone is present. In this work, we develop and evaluate deep learning methods for intrapatient CT-MR registration while maintaining rigidity of the bones. Unconstrained and locally constrained registration methods are compared in an unsupervised and weakly-supervised setting. The results show that qualitatively and quantitatively accurate registrations can be obtained.