GroupMorph: Medical Image Registration via Grouping Network With Contextual Fusion

Pyramid-based deformation decomposition is a promising registration framework, which gradually decomposes the deformation field into multi-resolution subfields for precise registration. However, most pyramid-based methods directly produce one subfield per resolution level, which does not fully depic...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2024-11, Vol.43 (11), p.3807-3819
Main Authors: Tan, Zuopeng, Zhang, Lihe, Lv, Yanan, Ma, Yili, Lu, Huchuan
Format: Article
Language:English
Subjects:
Algorithms
Brain - diagnostic imaging
contextual feature fusion
Convolution
Correlation
Databases, Factual
Deformable image registration
Deformation
deformation decomposition
Feature extraction
Humans
Image Processing, Computer-Assisted - methods
Image registration
Magnetic Resonance Imaging - methods
Spatial resolution
Transformers
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Summary:Pyramid-based deformation decomposition is a promising registration framework, which gradually decomposes the deformation field into multi-resolution subfields for precise registration. However, most pyramid-based methods directly produce one subfield per resolution level, which does not fully depict the spatial deformation. In this paper, we propose a novel registration model, called GroupMorph. Different from typical pyramid-based methods, we adopt the grouping-combination strategy to predict deformation field at each resolution. Specifically, we perform group-wise correlation calculation to measure the similarities of grouped features. After that, n groups of deformation subfields with different receptive fields are predicted in parallel. By composing these subfields, a deformation field with multi-receptive field ranges is formed, which can effectively identify both large and small deformations. Meanwhile, a contextual fusion module is designed to fuse the contextual features and provide the inter-group information for the field estimator of the next level. By leveraging the inter-group correspondence, the synergy among deformation subfields is enhanced. Extensive experiments on four public datasets demonstrate the effectiveness of GroupMorph. Code is available at https://github.com/TVayne/GroupMorph .
ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2024.3400603