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2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2008)
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-8
Xiaoxiao Liu , Medical Image Display&Analysis Group (MIDAG), University of North Carolina, Chapel Hill, 27599, USA
Stephen M. Pizer , Medical Image Display&Analysis Group (MIDAG), University of North Carolina, Chapel Hill, 27599, USA
Edward L. Chaney , Medical Image Display&Analysis Group (MIDAG), University of North Carolina, Chapel Hill, 27599, USA
Joshua H. Levy , Medical Image Display&Analysis Group (MIDAG), University of North Carolina, Chapel Hill, 27599, USA
Ja-Yeon Jeong , Medical Image Display&Analysis Group (MIDAG), University of North Carolina, Chapel Hill, 27599, USA
Rohit R. Saboo , Medical Image Display&Analysis Group (MIDAG), University of North Carolina, Chapel Hill, 27599, USA
ABSTRACT
Training a shape prior has been potent scheme for anatomical object segmentations, especially for images with noisy or weak intensity patterns. When the shape representation lives in a high dimensional space, principal component analysis is often used to calculate a low dimensional variation subspace from frequently limited number of training samples. However, the eigenmodes of the sub-space tend to keep the large-scale variation of the shape only, losing the detailed localized variability which is crucial to accurate segmentations. In this paper, we propose a large-to-fine-scale shape prior for probabilistic segmentation to enable local refinement, using a deformable medial representation, called the m-rep. Tests on the goodness of the shape prior are carried out on large simulated data sets of a) 1000 deformed ellipsoids with mixed global deformations and local perturbation; b) 500 simulated hippocampus models. The predictability of the shape priors are evaluated and compared by a squared correlations metric and the volume overlap measurement against different training sample sizes. The improved robustness achieved by the large-to-fine-scale strategy is demonstrated, especially for low sample size applications. Finally, posterior 3D segmentations of the bladder from CT images from multiple patients in day-to-day adaptive radiation therapy demonstrate that the local residual statistics introduced by this method improve the segmentation accuracy.
INDEX TERMS
CITATION
Xiaoxiao Liu, Stephen M. Pizer, Edward L. Chaney, Joshua H. Levy, Ja-Yeon Jeong, Rohit R. Saboo, "A large-to-fine-scale shape prior for probabilistic segmentations using a deformable m-rep", 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 00, no. , pp. 1-8, 2008, doi:10.1109/CVPRW.2008.4563019
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