research-article

Unsupervised deformable image registration network for 3D medical images

Authors:

Caiming ZhangAuthors Info & Claims

Applied Intelligence, Volume 52, Issue 1

Pages 766 - 779

https://doi.org/10.1007/s10489-021-02196-7

Published: 01 January 2022 Publication History

Abstract

Image registration aims to establish an active correspondence between a pair of images. Such correspondence is critical for many significant applications, such as image fusion, tumor growth monitoring, and atlas generation. In this study, we propose an unsupervised deformable image registration network (UDIR-Net) for 3D medical images. The proposed UDIR-Net is designed in an encoder-decoder architecture and directly estimates the complex deformation field between input pairwise images without any supervised information. In particular, we recalibrate the feature slice of each feature map that is propagated between the encoder and the decoder in accordance with the importance of each feature slice and the correlation between feature slices. This method enhances the representational power of feature maps. To achieve efficient and robust training, we design a novel hierarchical loss function that evaluates multiscale similarity loss between registered image pairs. The proposed UDIR-Net is tested on different public magnetic resonance image datasets of the human brain. Experimental results show that UDIR-Net exhibits competitive performance against several state-of-the-art methods.

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Cited By

Wang LShao HDeng X(2023)An Unsupervised End-to-End Recursive Cascaded Parallel Network for Image RegistrationNeural Processing Letters10.1007/s11063-023-11311-355:6(8255-8268)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11063-023-11311-3
Ma RDu X(2023)Closed-loop feedback registration for consecutive images of moving flexible targetsApplied Intelligence10.1007/s10489-022-04068-053:9(10647-10667)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1007/s10489-022-04068-0

Index Terms

Unsupervised deformable image registration network for 3D medical images
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Applied Intelligence

Applied Intelligence Volume 52, Issue 1

Jan 2022

1143 pages

ISSN:0924-669X

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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2022

Accepted: 05 January 2021

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Cited By

Wang LShao HDeng X(2023)An Unsupervised End-to-End Recursive Cascaded Parallel Network for Image RegistrationNeural Processing Letters10.1007/s11063-023-11311-355:6(8255-8268)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11063-023-11311-3
Ma RDu X(2023)Closed-loop feedback registration for consecutive images of moving flexible targetsApplied Intelligence10.1007/s10489-022-04068-053:9(10647-10667)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1007/s10489-022-04068-0

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