Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching
Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to deriv...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2015/125648 |
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| author | Gurman Gill Reinhard R. Beichel |
| author_facet | Gurman Gill Reinhard R. Beichel |
| author_sort | Gurman Gill |
| collection | DOAJ |
| description | Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of 0.9773±0.0254, which was statistically significantly better (p value ≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes. |
| format | Article |
| id | doaj-art-a2ba9f89ed574ba2af62eea3e16b40f6 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-a2ba9f89ed574ba2af62eea3e16b40f62025-02-03T01:02:25ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962015-01-01201510.1155/2015/125648125648Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model MatchingGurman Gill0Reinhard R. Beichel1Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USADepartment of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USADynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of 0.9773±0.0254, which was statistically significantly better (p value ≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes.http://dx.doi.org/10.1155/2015/125648 |
| spellingShingle | Gurman Gill Reinhard R. Beichel Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching International Journal of Biomedical Imaging |
| title | Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching |
| title_full | Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching |
| title_fullStr | Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching |
| title_full_unstemmed | Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching |
| title_short | Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching |
| title_sort | lung segmentation in 4d ct volumes based on robust active shape model matching |
| url | http://dx.doi.org/10.1155/2015/125648 |
| work_keys_str_mv | AT gurmangill lungsegmentationin4dctvolumesbasedonrobustactiveshapemodelmatching AT reinhardrbeichel lungsegmentationin4dctvolumesbasedonrobustactiveshapemodelmatching |