Journal article
Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach
IEEE transactions on medical imaging, Vol.31(2), pp.449-460
02/2012
DOI: 10.1109/TMI.2011.2171357
PMCID: PMC3657761
PMID: 21997248
Abstract
Segmentation of lungs with (large) lung cancer regions is a nontrivial problem. We present a new fully automated approach for segmentation of lungs with such high-density pathologies. Our method consists of two main processing steps. First, a novel robust active shape model (RASM) matching method is utilized to roughly segment the outline of the lungs. The initial position of the RASM is found by means of a rib cage detection method. Second, an optimal surface finding approach is utilized to further adapt the initial segmentation result to the lung. Left and right lungs are segmented individually. An evaluation on 30 data sets with 40 abnormal (lung cancer) and 20 normal left/right lungs resulted in an average Dice coefficient of 0.975 ± 0.006 and a mean absolute surface distance error of 0.84 ± 0.23 mm, respectively. Experiments on the same 30 data sets showed that our methods delivered statistically significant better segmentation results, compared to two commercially available lung segmentation approaches. In addition, our RASM approach is generally applicable and suitable for large shape models.
Details
- Title: Subtitle
- Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach
- Creators
- Shanhui Sun - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242 USAChristian Bauer - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242 USAReinhard Beichel - Department of Internal Medicine and the Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242 USA
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on medical imaging, Vol.31(2), pp.449-460
- DOI
- 10.1109/TMI.2011.2171357
- PMID
- 21997248
- PMCID
- PMC3657761
- NLM abbreviation
- IEEE Trans Med Imaging
- ISSN
- 0278-0062
- eISSN
- 1558-254X
- Publisher
- Institute of Electrical and Electronics Engineers
- Grant note
- R01 EB004640 || EB / National Institute of Biomedical Imaging and Bioengineering : NIBIB
- Language
- English
- Date published
- 02/2012
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984083993302771
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