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Bayesian Segmentation of Atrium Wall using Globally-optimal Graph Cuts on 3D Meshes

Institution:
Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, USA.
Publisher:
Inf Process Med Imaging IPMI 2013
Publication Date:
Jun-2013
Journal:
Inf Process Med Imaging
Volume Number:
23
Pages:
656-67
Citation:
Inf Process Med Imaging. 2013 Jun;23:656-67.
PubMed ID:
24684007
PMCID:
PMC4157062
Keywords:
Atrial Fibrillation, Bayesian segmentation, Minimum s-t cut, Mesh Generation, Geometric Graph
Appears in Collections:
NA-MIC
Sponsors:
P41 GM103545/GM/NIGMS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Veni G., Fu Z., Awate S.P., Whitaker R.T. Bayesian Segmentation of Atrium Wall using Globally-optimal Graph Cuts on 3D Meshes. Inf Process Med Imaging. 2013 Jun;23:656-67. PMID: 24684007. PMCID: PMC4157062.
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Efficient segmentation of the left atrium (LA) wall from delayed enhancement MRI is challenging due to inconsistent contrast, combined with noise, and high variation in atrial shape and size. We present a surface-detection method that is capable of extracting the atrial wall by computing an optimal a-posteriori estimate. This estimation is done on a set of nested meshes, constructed from an ensemble of segmented training images, and graph cuts on an associated multi-column, proper-ordered graph. The graph/mesh is a part of a template/model that has an associated set of learned intensity features. When this mesh is overlaid onto a test image, it produces a set of costs which lead to an optimal segmentation. The 3D mesh has an associated weighted, directed multi-column graph with edges that encode smoothness and inter-surface penalties. Unlike previous graph-cut methods that impose hard constraints on the surface properties, the proposed method follows from a Bayesian formulation resulting in soft penalties on spatial variation of the cuts through the mesh. The novelty of this method also lies in the construction of proper-ordered graphs on complex shapes for choosing among distinct classes of base shapes for automatic LA segmentation. We evaluate the proposed segmentation framework on simulated and clinical cardiac MRI.

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