Steger, Sebastian ; Kirschner, Matthias ; Wesarg, Stefan (2012)
Articulated Atlas for Segmentation of the Skeleton from Head & Neck CT Datasets.
2012 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
doi: 10.1109/ISBI.2012.6235790
Conference or Workshop Item, Bibliographie
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 ; Kirschner, Matthias ; Wesarg, Stefan |
| Type of entry: | Bibliographie |
| Title: | Articulated Atlas for Segmentation of the Skeleton from Head & Neck CT Datasets |
| Language: | English |
| Date: | 2012 |
| Publisher: | IEEE Press, New York |
| Event Title: | 2012 IEEE International Symposium on Biomedical Imaging: From Nano to Macro |
| DOI: | 10.1109/ISBI.2012.6235790 |
| 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. |
| 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 |
| Date Deposited: | 12 Nov 2018 11:16 |
| Last Modified: | 12 Nov 2018 11:16 |
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