Vector field attention for deformable image registration

Yihao Liu; Junyu Chen; Lianrui Zuo; Aaron Carass; Jerry L. Prince

doi:10.1117/1.JMI.11.6.064001

6 November 2024 Vector field attention for deformable image registration

Yihao Liu, Junyu Chen, Lianrui Zuo, Aaron Carass, Jerry L. Prince

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 6, 064001 (November 2024). https://doi.org/10.1117/1.JMI.11.6.064001

Abstract

Purpose

Deformable image registration establishes non-linear spatial correspondences between fixed and moving images. Deep learning–based deformable registration methods have been widely studied in recent years due to their speed advantage over traditional algorithms as well as their better accuracy. Most existing deep learning–based methods require neural networks to encode location information in their feature maps and predict displacement or deformation fields through convolutional or fully connected layers from these high-dimensional feature maps. We present vector field attention (VFA), a novel framework that enhances the efficiency of the existing network design by enabling direct retrieval of location correspondences.

Approach

VFA uses neural networks to extract multi-resolution feature maps from the fixed and moving images and then retrieves pixel-level correspondences based on feature similarity. The retrieval is achieved with a novel attention module without the need for learnable parameters. VFA is trained end-to-end in either a supervised or unsupervised manner.

Results

We evaluated VFA for intra- and inter-modality registration and unsupervised and semi-supervised registration using public datasets as well as the Learn2Reg challenge. VFA demonstrated comparable or superior registration accuracy compared with several state-of-the-art methods.

Conclusions

VFA offers a novel approach to deformable image registration by directly retrieving spatial correspondences from feature maps, leading to improved performance in registration tasks. It holds potential for broader applications.

Citation Download Citation

Yihao Liu, Junyu Chen, Lianrui Zuo, Aaron Carass, and Jerry L. Prince "Vector field attention for deformable image registration," Journal of Medical Imaging 11(6), 064001 (6 November 2024). https://doi.org/10.1117/1.JMI.11.6.064001

Received: 17 July 2024; Accepted: 16 October 2024; Published: 6 November 2024

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KEYWORDS

Image registration

Feature extraction

Deformation

Education and training

Visualization

Magnetic resonance imaging

Matrices

Purpose

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