Name
Abstract | ||
|---|---|---|
Registration uncertainty may be important information to convey to a surgeon when surgical decisions are taken based on registered image data. However, conventional non-rigid registration methods only provide the most likely deformation. In this paper we show how to determine the registration uncertainty, as well as the most likely deformation, by using an elastic Bayesian registration framework that generates a dense posterior distribution on deformations. We model both the likelihood and the elastic prior on deformations with Boltzmann distributions and characterize the posterior with a Markov Chain Monte Carlo algorithm. We introduce methods that summarize the high-dimensional uncertainty information and show how these summaries can be visualized in a meaningful way. Based on a clinical neurosurgical dataset, we demonstrate the importance that uncertainty information could have on neurosurgical decision making. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15745-5_68 | MICCAI (2) |
Keywords | Field | DocType |
important information,conventional non-rigid registration method,high-dimensional uncertainty information,neurosurgical decision,uncertainty information,registration uncertainty,visualizing uncertainty,likely deformation,dense posterior distribution,clinical neurosurgical dataset,elastic bayesian registration framework,posterior distribution,markov chain monte carlo,boltzmann distribution | Computer vision,Markov chain Monte Carlo,Computer science,Markov chain monte carlo algorithm,Posterior probability,Artificial intelligence,Boltzmann constant,Bayesian probability | Conference |
Volume | Issue | ISSN |
13 | Pt 2 | 0302-9743 |
ISBN | Citations | PageRank |
3-642-15744-0 | 29 | 1.81 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Petter Risholm | 1 | 109 | 10.71 |
| Steve Pieper | 2 | 244 | 33.32 |
| Eigil Samset | 3 | 133 | 16.57 |
| William M. Wells III | 4 | 5267 | 833.10 |