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Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
Open accessAbstract
Traditional deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have facilitated fast registration by learning spatial deformation functions. However, these approaches use restricted deformation models, require supervised labels, or do not guarantee a diffeomorphic (topology-preserving) registration. Furthermore, learning-based registration tools have not been derived from a probabilistic framework that can offer uncertainty estimates. In this paper, we present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs). We demonstrate our method on a 3D brain registration task, and provide an empirical analysis of the algorithm. Our approach results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees and uncertainty estimates. Our implementation is available online at this http URL .
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Background
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Cited by
Deep learning in medical image registration: a review
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Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Dalca, A. V., Balakrishnan, G., Guttag, J., & Sabuncu, M. R. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.07.006 · Cited by 467
Self-citationCycleMorph: Cycle consistent unsupervised deformable image registration
Kim, B., Kim, D. H., Park, S. H., Kim, J., Lee, J.-G., & Ye, J. C. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102036 · Cited by 336
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Self-citationMedical image registration using deep neural networks: A comprehensive review
Boveiri, H. R., Khayami, R., Javidan, R., & Mehdizadeh, A. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106767 · Cited by 179
Metadata
- Authors
- Dalca, A. V., Balakrishnan, G., Guttag, J., & Sabuncu, M. R.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 729-738
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2799913653
- Cited by
- 354
- References
- 12
- FWCI
- 13.62
- Type
- conference paper
- Language
- en
- Open access
- green
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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