Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching
Joint Authors
Gill, Gurman
Beichel, Reinhard R.
Source
International Journal of Biomedical Imaging
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-08
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Gill, Gurman& Beichel, Reinhard R.. 2015. Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching. International Journal of Biomedical Imaging،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1065276
Modern Language Association (MLA)
Gill, Gurman& Beichel, Reinhard R.. Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching. International Journal of Biomedical Imaging No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1065276
American Medical Association (AMA)
Gill, Gurman& Beichel, Reinhard R.. Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching. International Journal of Biomedical Imaging. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1065276
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1065276