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

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A Bayesian Model for Joint Segmentation and Registration

Institution:
Surgical Planning Laboratory, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA.
Publisher:
Neuroimage
Publication Date:
May-2006
Volume Number:
31
Issue Number:
1
Pages:
228-39
Citation:
Neuroimage. 2006 May 15;31(1):228-39.
PubMed ID:
16466677
Keywords:
Registration, Segmentation, Subcortical segmentation, Bayesian Modeling, Expectation-Maximization, Projects:ShapeBasedSegmentationAndRegistration
Appears in Collections:
SPL, NA-MIC, NAC
Sponsors:
P41 RR013218/RR/NCRR NIH HHS/United States
R01 NS051826/NS/NINDS NIH HHS/United States
U24 RR021382/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Pohl K.M., Fisher III J.W., Grimson W.E.L., Kikinis R., Wells III W.M. A Bayesian Model for Joint Segmentation and Registration. Neuroimage. 2006 May 15;31(1):228-39. PMID: 16466677.
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A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation.

Additional Material
1 File (158.395kB)
Pohl-NeuroImage2006-fig5.jpg (158.395kB)