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Towards Generalised Neural Implicit Representations for Image Registration

MCML Authors

Link to Profile Julia Schnabel

Julia Schnabel

Prof. Dr.

Core PI

Computational Imaging and AI in Medicine

Abstract

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 and datasets. Our results demonstrate the potential of generalised NIRs for 3D deformable image registration.

inproceedings ZHS+23

DGM4 @MICCAI 2023

3rd International Workshop on Deep Generative Models at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention. Vancouver, Canada, Oct 08-12, 2023.

Authors

V. A. Zimmer • K. Hammernik • V. Sideri-Lampretsa • W. Huang • A. Reithmeir • D. Rückert • J. A. Schnabel

Links

DOI

Research Area

 C1 | Medicine

BibTeXKey: ZHS+23

#p-rueckert #p-schnabel
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