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Contour-driven Regression for Label Inference in Atlas-based Segmentation

Institution:
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA, USA.
2Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2013
Publication Date:
Sep-2013
Volume Number:
16
Issue Number:
Pt 3
Pages:
211-8
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 3):211-8.
PubMed ID:
24505763
PMCID:
PMC3935362
Appears in Collections:
NAC, NA-MIC
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
Generated Citation:
Wachinger C., Sharp G.C., Golland P. Contour-driven Regression for Label Inference in Atlas-based Segmentation. Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 3):211-8. PMID: 24505763. PMCID: PMC3935362.
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We present a novel method for inferring tissue labels in atlas-based image segmentation using Gaussian process regression. Atlas-based segmentation results in probabilistic label maps that serve as input to our method. We introduce a contour-driven prior distribution over label maps to incorporate image features of the input scan into the label inference problem. The mean function of the Gaussian process posterior distribution yields the MAP estimate of the label map and is used in the subsequent voting. We demonstrate improved segmentation accuracy when our approach is combined with two different patch-based segmentation techniques. We focus on the segmentation of parotid glands in CT scans of patients with head and neck cancer, which is important for radiation therapy planning.

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Wachinger-MICCAI2013-fig4.jpg (132.842kB)