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"Chapter 19 - Machine learning in image registration"
Bob D. de Vos, Hessam Sokooti, Marius Staring and Ivana Išgum
Abstract
Image registration, the task of aligning images, has been one of the key methods in medical image analysis. The goal in image registration is to find a coordinate mapping between a fixed target image and a moving source image. Until recently, machine learning was mainly used to aid image registration, e.g., for detecting misalignment. Nowadays, machine learning methods perform image registration themselves. In particular, deep learning-based image registration methods are gradually taking over the slow old methods. The benefit of machine learning-based image registration is that the spatial statistical relation between images are learned in an offline training phase. This means that during online inference time, image registration is performed very rapidly in one shot. In this chapter we will explain how tow train global and local image registration methods with supervised and unsupervised machine learning.
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Copyright © 2024 by the authors.
Published version © 2024 by .
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @incollection{deVos:2024, |
| author |
= {de Vos, Bob D. and Sokooti, Hessam and Staring, Marius and Išgum, Ivana}, |
| title |
= {Chapter 19 - Machine learning in image registration}, |
| booktitle |
= {Medical Image Analysis}, |
| editor |
= {Frangi, Alejandro F. and Prince, Jerry L. and Sonka, Milan}, |
| publisher |
= {Academic Press}, |
| series |
= {The MICCAI Society book Series}, |
| chapter |
= {19}, |
| pages |
= {501 - 515}, |
| year |
= {2024}, |
| } |
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| last modified: 05-10-2023 |
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