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Deformable 3D Brain MRI Registration with Deep Learning
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Deformerbar 3D MRI-registrering med djupinlärning (Swedish)
Abstract [en]

Traditional deformable registration methods have achieved impressive performances but are computationally time-consuming since they have to optimize an objective function for each new pair of images. Very recently some learning-based approaches have been proposed to enable fast registration by learning to estimate the spatial transformation parameters directly from the input images. Here we present a method for 3D fast pairwise registration of brain MR images. We model the deformation function with B-splines and learn the optimal control points using a U-Net like CNN architecture. An inverse-consistency loss has been used to enforce diffeomorphicity of the deformation. The proposed algorithm does not require supervised information such as segmented labels but some can be used to help the registration process. We also implemented several strategies to account for the multi-resolution nature of the problem. The method has been evaluated on MICCAI 2012 brain MRI datasets, and evaluated on both similarity and invertibility of the computed transformation.

Place, publisher, year, edition, pages
2019. , p. 40
Series
TRITA-CBH-GRU ; 2019:106
Keywords [en]
Deep Learning, Image Registration
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-262852OAI: oai:DiVA.org:kth-262852DiVA, id: diva2:1362867
External cooperation
CREATIS Laboratory
Subject / course
Medical Engineering
Educational program
Master of Science - Medical Engineering
Supervisors
Examiners
Available from: 2019-10-23 Created: 2019-10-21 Last updated: 2019-10-23Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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