Journal article
Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach
International journal of biomedical imaging, Vol.2014(2014), pp.1-7
2014
DOI: 10.1155/2014/479154
PMCID: PMC4221988
PMID: 25400660
Abstract
Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation.
Details
- Title: Subtitle
- Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach
- Creators
- Gurman Gill - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA, The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USAMatthew Toews - Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USAReinhard R Beichel - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA, The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA, Department of Internal Medicine, The University of Iowa, Iowa City, IA 52242, USA
- Resource Type
- Journal article
- Publication Details
- International journal of biomedical imaging, Vol.2014(2014), pp.1-7
- DOI
- 10.1155/2014/479154
- PMID
- 25400660
- PMCID
- PMC4221988
- NLM abbreviation
- Int J Biomed Imaging
- ISSN
- 1687-4188
- eISSN
- 1687-4196
- Grant note
- DOI: 10.13039/100000009, name: Foundation for the National Institutes of Health, award: R01HL111453, P41EB015902, P41EB015898
- Language
- English
- Date published
- 2014
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984083841102771
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