Journal article
Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching
International journal of biomedical imaging, Vol.2015, pp.125648-9
10/08/2015
DOI: 10.1155/2015/125648
PMCID: PMC4618332
PMID: 26557844
Abstract
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.
Details
- Title: Subtitle
- Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching
- Creators
- Gurman Gill - Department of Electrical and Computer Engineering The University of Iowa Iowa City IA 52242 USA uiowa.eduReinhard R Beichel - Department of Electrical and Computer Engineering The University of Iowa Iowa City IA 52242 USA uiowa.edu
- Contributors
- Jyh-Cheng Chen (Editor)
- Resource Type
- Journal article
- Publication Details
- International journal of biomedical imaging, Vol.2015, pp.125648-9
- DOI
- 10.1155/2015/125648
- PMID
- 26557844
- PMCID
- PMC4618332
- NLM abbreviation
- Int J Biomed Imaging
- ISSN
- 1687-4188
- eISSN
- 1687-4196
- Publisher
- Hindawi Publishing Corporation
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health, award: R01HL111453
- Language
- English
- Date published
- 10/08/2015
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984083229102771
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