Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Summarizing and Visualizing Registration Uncertainty in Non-Rigid Registration

Institution:
1Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2Center of Mathematics for Applications, University of Oslo, Norway.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2010
Publication Date:
Sep-2010
Volume Number:
6362
Pages:
554-61
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2010 Sep;13(Pt 2):554-61.
Presented at:
MICCAI 2010
Links:
http://www.spl.harvard.edu/publications/item/view/1915
PubMed ID:
20879359
PMCID:
PMC2976974
ISBN:
978-3-642-15744-8
Appears in Collections:
SPL, NA-MIC, NAC, NCIGT
Sponsors:
R01 CA138419/CA/NCI NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
P41 RR019703/RR/NCRR NIH HHS/United States
Generated Citation:
Risholm P., Pieper S., Samset E., Wells III W.M. Summarizing and Visualizing Registration Uncertainty in Non-Rigid Registration. Int Conf Med Image Comput Comput Assist Interv. 2010 Sep;13(Pt 2):554-61. PMID: 20879359. PMCID: PMC2976974.
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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.

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