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TransMorph: Transformer for unsupervised medical image registration.
- Source :
-
Medical image analysis [Med Image Anal] 2022 Nov; Vol. 82, pp. 102615. Date of Electronic Publication: 2022 Sep 14. - Publication Year :
- 2022
-
Abstract
- 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.<br />Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2022 Elsevier B.V. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1361-8423
- Volume :
- 82
- Database :
- MEDLINE
- Journal :
- Medical image analysis
- Publication Type :
- Academic Journal
- Accession number :
- 36156420
- Full Text :
- https://doi.org/10.1016/j.media.2022.102615