Contour-Driven Atlas-Based Segmentation
Author(s)
Wachinger, Christian; Fritscher, Karl; Sharp, Greg; Golland, Polina![Thumbnail](/bitstream/handle/1721.1/111005/Contour-driven.pdf.jpg?sequence=4&isAllowed=y)
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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.
Date issued
2015-12Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Wachinger, Christian et al. “Contour-Driven Atlas-Based Segmentation.” IEEE Transactions on Medical Imaging 34, 12 (December 2015): 2492–2505 © 2015 Institute of Electrical and Electronics Engineers (IEEE)
Version: Author's final manuscript
ISSN
0278-0062
1558-254X