Metric Learning for Image Registration

Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture o...

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Vydané v:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Ročník 2019; s. 8455 - 8464
Hlavní autori: Niethammer, Marc, Kwitt, Roland, Vialard, Francois-Xavier
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.06.2019
Predmet:
Adaptation models
Biological and Cell Microscopy
Computational modeling
Deep learning
Deformable models
Image registration
Medical
Motion and Tracking
Predictive models
Training
ISSN:1063-6919, 1063-6919
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Popis
Shrnutí:Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.
Bibliografia:ObjectType-Article-1
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2019.00866