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Towards generalised neural implicit representations for image registration.
In:. Berlin [u.a.]: Springer, 2024. 45-55 (Lect. Notes Comput. Sc. ; 14533 LNCS)
Neural implicit representations (NIRs) enable to generate and parametrize the transformation for image registration in a continuous way. By design, these representations are image-pair-specific, meaning that for each signal a new multi-layer perceptron has to be trained. In this work, we investigate for the first time the potential of existent NIR generalisation methods for image registration and propose novel methods for the registration of a group of image pairs using NIRs. To exploit the generalisation potential of NIRs, we encode the fixed and moving image volumes to latent representations, which are then used to condition or modulate the NIR. Using ablation studies on a 3D benchmark dataset, we show that our methods are able to generalise to a set of image pairs with a performance comparable to pairwise registration using NIRs when trained on N=10 and N=120 datasets. Our results demonstrate the potential of generalised NIRs for 3D deformable image registration.
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Publication type
Article: Conference contribution
Keywords
Generalisation ; Image Registration ; Neural Implicit Representation ; Periodic Activation Functions
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Quellenangaben
Volume: 14533 LNCS,
Pages: 45-55
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)