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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Bibliographic Details
Published in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Vol. 2019; pp. 8455 - 8464
Main Authors: Niethammer, Marc, Kwitt, Roland, Vialard, Francois-Xavier
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.06.2019
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2019.00866