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Articulated Atlas for Segmentation of the Skeleton from Head & Neck CT Datasets

Steger, Sebastian and Kirschner, Matthias and Wesarg, Stefan (2012):
Articulated Atlas for Segmentation of the Skeleton from Head & Neck CT Datasets.
pp. 1256-1259, IEEE Press, New York, 2012 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, DOI: 10.1109/ISBI.2012.6235790,
[Conference or Workshop Item]

Abstract

In this paper a novel articulated atlas for the fully automated segmentation of the skeleton from head & neck CT datasets is presented. An individual atlas describing the shape and appearance is created for each individual bone. Principal Component Analysis is used to learn spatial relations between those atlases resulting in a unified articulated atlas. Transformations are parameterized using the matrix exponential to enable linear combinations required for learning. The adaptation to test images considers appearance, distance to bone structures and the trained articulation space. For evaluation, an atlas created from 10 manually labeled training images has been applied to 46 clinically acquired head & neck CT datasets. Visual inspection showed that in 74 of the cases, the adaptation process was successful. In a second experiment leave-one-out validation was used to quantify the segmentation accuracy. The successfully adapted cases resulted in an average volume overlap error of 30.67 and an average symmetric surface distance of 0.76 mm.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Steger, Sebastian and Kirschner, Matthias and Wesarg, Stefan
Title: Articulated Atlas for Segmentation of the Skeleton from Head & Neck CT Datasets
Language: English
Abstract:

In this paper a novel articulated atlas for the fully automated segmentation of the skeleton from head & neck CT datasets is presented. An individual atlas describing the shape and appearance is created for each individual bone. Principal Component Analysis is used to learn spatial relations between those atlases resulting in a unified articulated atlas. Transformations are parameterized using the matrix exponential to enable linear combinations required for learning. The adaptation to test images considers appearance, distance to bone structures and the trained articulation space. For evaluation, an atlas created from 10 manually labeled training images has been applied to 46 clinically acquired head & neck CT datasets. Visual inspection showed that in 74 of the cases, the adaptation process was successful. In a second experiment leave-one-out validation was used to quantify the segmentation accuracy. The successfully adapted cases resulted in an average volume overlap error of 30.67 and an average symmetric surface distance of 0.76 mm.

Publisher: IEEE Press, New York
Uncontrolled Keywords: Business Field: Digital society, Business Field: Visual decision support, Research Area: Confluence of graphics and vision, Statistical shape models (SSM), Segmentation, Articulated atlas
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: 2012 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1109/ISBI.2012.6235790
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