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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 1-2/2020

01.02.2020 | Original Paper

Deep learning in medical image registration: a survey

verfasst von: Grant Haskins, Uwe Kruger, Pingkun Yan

Erschienen in: Machine Vision and Applications | Ausgabe 1-2/2020

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Abstract

The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning-based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, which is the main scope of this survey. This requires placing a focus on the different research areas as well as highlighting challenges that practitioners face. This survey, therefore, outlines the evolution of deep learning-based medical image registration in the context of both research challenges and relevant innovations in the past few years. Further, this survey highlights future research directions to show how this field may be possibly moved forward to the next level.
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Metadaten
Titel
Deep learning in medical image registration: a survey
verfasst von
Grant Haskins
Uwe Kruger
Pingkun Yan
Publikationsdatum
01.02.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 1-2/2020
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-020-01060-x

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