Bayesian Characterization of Uncertainty in Intra-subject Non-rigid Registration
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In settings where high-level inferences are made based on registered image data, the registration uncertainty can contain important information. In this article, we propose a Bayesian non-rigid registration framework where conventional dissimilarity and regularization energies can be included in the likelihood and the prior distribution on deformations respectively through the use of Boltzmann's distribution. The posterior distribution is characterized using Markov Chain Monte Carlo (MCMC) methods with the effect of the Boltzmann temperature hyper-parameters marginalized under broad uninformative hyper-prior distributions. The MCMC chain permits estimation of the most likely deformation as well as the associated uncertainty. On synthetic examples, we demonstrate the ability of the method to identify the maximum a posteriori estimate and the associated posterior uncertainty, and demonstrate that the posterior distribution can be non-Gaussian. Additionally, results from registering clinical data acquired during neurosurgery for resection of brain tumor are provided; we compare the method to single transformation results from a deterministic optimizer and introduce methods that summarize the high-dimensional uncertainty. At the site of resection, the registration uncertainty increases and the marginal distribution on deformations is shown to be multi-modal.
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Some uncertainty estimation approaches report interval estimates by reformulating the registration objective as a probability distribution and drawing samples, using test-time NN drop-out, or sampling using test-time deformation covariance matrices in a variational autoencoder.
“Some of these approaches report interval estimates, either by reformulating the registration objective function as a probability distribution and drawing samples (Risholm et al., 2013; Le Folgoc et al., 2016; Schultz et al., 2018), approximating the sampling process using test-time NN drop-out (Yang et al., 2017) or sampling using the test-time deformation covariance matrices embedded within a”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Risholm et al., 2013
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- Authors
- Risholm, P., Janoos, F., Norton, I., Golby, A. J., & Wells, W. M.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Risholm, Petter
- ORCID
- Wells, William M.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2013-07
- Volume
- 17
- Issue
- 5
- Pages
- 538-555
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC3687087
- OpenAlex
- W2082030398
- Cited by
- 62
- References
- 62
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2022: 3 · 2021: 6 · 2020: 4 · 2019: 8 · 2018: 7 · 2017: 4 · 2016: 8 · 2015: 5 · 2014: 6
- FWCI
- 6.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Cancer Institute (R01CA138419)
- National Institute of Biomedical Imaging and Bioengineering (P41EB015902, P41EB015898)
- National Center for Research Resources (P41RR019703, P41RR013218)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- deformable image registration, registration uncertainty, Boltzmann distribution, Markov chain Monte Carlo, maximum a posteriori estimation, multimodal posterior distributions, uncertainty quantification
- MeSH terms
- Algorithms, Bayes Theorem, Brain, Data Interpretation, Statistical, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Imaging, Three-Dimensional
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