Fast Predictive Image Registration
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Yang et al. proposed to register brain MRI images using a U-Net-like structure in one step.
“At first, Yang et al. (73) proposed to register brain MRI images using a U-Net-like structure in one step.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [73]
The Network Structure section (p. 3) describes an encoder–decoder network using patches from fixed and moving images, and the paper tests it on brain images (Experiments, p. 5). But the network predicts LDDMM initial momentum, from which the deformation is computed by integration (Initial Momentum LDDMM Parameterization, p. 2); the copy does not establish that registration is completed in one step. It calls the encoder VGG-style rather than U-Net-like, though the encoder–decoder description supports that resemblance. (LLM, conf=0.85)
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- Authors
- Yang, X., Kwitt, R., & Niethammer, M.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- University of Salzburg (AT)
- Corresponding author
- Yang, Xiao
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 48-57
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2482922118
- Cited by
- 127
- References
- 20
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 10 · 2023: 17 · 2022: 14 · 2021: 18 · 2020: 14 · 2019: 23 · 2018: 8 · 2017: 17
- FWCI
- 5.55
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- image registration, deformation prediction, LDDMM, diffeomorphic registration, Bayesian neural networks, uncertainty estimation, dropout, brain MRI registration, OASIS dataset
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