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TransMorph: Transformer for unsupervised medical image registration.

Publication ,  Journal Article
Chen, J; Frey, EC; He, Y; Segars, WP; Li, Y; Du, Y
Published in: Med Image Anal
November 2022

In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently, Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehension of the spatial correspondence between moving and fixed images. Here, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. This paper also presents diffeomorphic and Bayesian variants of TransMorph: the diffeomorphic variants ensure the topology-preserving deformations, and the Bayesian variant produces a well-calibrated registration uncertainty estimate. We extensively validated the proposed models using 3D medical images from three applications: inter-patient and atlas-to-patient brain MRI registration and phantom-to-CT registration. The proposed models are evaluated in comparison to a variety of existing registration methods and Transformer architectures. Qualitative and quantitative results demonstrate that the proposed Transformer-based model leads to a substantial performance improvement over the baseline methods, confirming the effectiveness of Transformers for medical image registration.

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Published In

Med Image Anal

DOI

EISSN

1361-8423

Publication Date

November 2022

Volume

82

Start / End Page

102615

Location

Netherlands

Related Subject Headings

  • Phantoms, Imaging
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Magnetic Resonance Imaging
  • Imaging, Three-Dimensional
  • Image Processing, Computer-Assisted
  • Humans
  • Bayes Theorem
  • 40 Engineering
  • 32 Biomedical and clinical sciences
 

Citation

APA
Chicago
ICMJE
MLA
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Chen, J., Frey, E. C., He, Y., Segars, W. P., Li, Y., & Du, Y. (2022). TransMorph: Transformer for unsupervised medical image registration. Med Image Anal, 82, 102615. https://doi.org/10.1016/j.media.2022.102615
Chen, Junyu, Eric C. Frey, Yufan He, William P. Segars, Ye Li, and Yong Du. “TransMorph: Transformer for unsupervised medical image registration.Med Image Anal 82 (November 2022): 102615. https://doi.org/10.1016/j.media.2022.102615.
Chen J, Frey EC, He Y, Segars WP, Li Y, Du Y. TransMorph: Transformer for unsupervised medical image registration. Med Image Anal. 2022 Nov;82:102615.
Chen, Junyu, et al. “TransMorph: Transformer for unsupervised medical image registration.Med Image Anal, vol. 82, Nov. 2022, p. 102615. Pubmed, doi:10.1016/j.media.2022.102615.
Chen J, Frey EC, He Y, Segars WP, Li Y, Du Y. TransMorph: Transformer for unsupervised medical image registration. Med Image Anal. 2022 Nov;82:102615.
Journal cover image

Published In

Med Image Anal

DOI

EISSN

1361-8423

Publication Date

November 2022

Volume

82

Start / End Page

102615

Location

Netherlands

Related Subject Headings

  • Phantoms, Imaging
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Magnetic Resonance Imaging
  • Imaging, Three-Dimensional
  • Image Processing, Computer-Assisted
  • Humans
  • Bayes Theorem
  • 40 Engineering
  • 32 Biomedical and clinical sciences