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SEN-FCB: an unsupervised twinning neural network for image registration

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Abstract

Medical image registration is a fundamental and vital task in medical image analysis. Deformable medical image registration generates a dense nonlinear transformation from the moving image to the fixed image. Current learning-based image registration methods utilize U-shaped networks, concatenate moving and fixed images as one input, and then impose a global regularization to ensure smooth deformation fields. However, existing deformable image registration approaches concatenate image pairs as one input to their model and may ignore independent anatomical relevance of the images. Moreover, the global regularization causes over/underconstraining, affecting their model registration accuracy and over/under enforcing the deformation field’s smoothness. To address these two problems, we propose a twinning network, consisting of two subnetworks. The first subnetwork is the proposed separate encoding neural network (SEN) for predicting high-accuracy deformation fields, and the second subnetwork is a folding correction block (FCB) to correct the deformation fields to achieve folding reduction. Comparing our experimental results to the state-of-the-art displacement and diffeomorphic methods, the proposed method provides superior registration accuracy and reduces the folding numbers. Moreover, we utilize the FCB to correct the baselines’ output deformation fields, proving that the FCB outperforms global regularization.

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Code Availability

Our code is available at https://github.com/MingR-Ma/SEN-FCB.

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Acknowledgments

This work is supported by The National Nature Science Foundation of China (Grant Nos. 61772226 and 61862056), The Natural Science Foundation of Jilin Province (Grant number No. 20210204133YY), Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China, Jilin University.

Funding

This work was supported by the National Nature Science Foundation of China [grant number 61772226, 61862056]; Science and Technology Development Program of Jilin Province [grant number 20210204133YY]; The Natural Science Foundation of Jilin Province (Grant number No. 20200201159JC); Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China,Jilin University.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin, People’s Republic of China

    Mingrui Ma, Guixia Liu, Lei Song & Yuanbo Xu

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, People’s Republic of China

    Mingrui Ma, Guixia Liu, Lei Song & Yuanbo Xu

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  1. Mingrui Ma
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Lei Song, Mingrui Ma and Guixia Liu. The first draft of the manuscript was written by Lei Song and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Guixia Liu.

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The authors have no conflicts of interest to declare that are relevant to the content of this article.

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The data is a public data set and it can be obtained https://www.oasis-brains.org/. We preprocessed the data.

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Guixia Liu, Lei Song and Yuanbo Xu are contributed equally to this work.

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Ma, M., Liu, G., Song, L. et al. SEN-FCB: an unsupervised twinning neural network for image registration. Appl Intell 53, 12198–12209 (2023). https://doi.org/10.1007/s10489-022-04109-8

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