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Contour-Driven Atlas-Based Segmentation

Institution:
Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge MA, USA.
Publisher:
IEEE Engineering in Medicine and Biology Society EMBS 2015
Publication Date:
Dec-2015
Journal:
IEEE Trans Med Imaging
Volume Number:
34
Issue Number:
12
Pages:
2492-505
Citation:
IEEE Trans Med Imaging. 2015 Dec;34(12):2492-505.
PubMed ID:
26068202
PMCID:
PMC4756595
Keywords:
Atlas, Gaussian process, image segmentation, left atrium, parotid glands, spectral clustering
Appears in Collections:
NAC, NA-MIC
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Wachinger C., Fritscher K., Sharp G., Golland P. Contour-Driven Atlas-Based Segmentation. IEEE Trans Med Imaging. 2015 Dec;34(12):2492-505. PMID: 26068202. PMCID: PMC4756595.
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We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images.

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