Document Detail


An abdominal aortic aneurysm segmentation method: level set with region and statistical information.
MedLine Citation:
PMID:  16752579     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
We present a system for segmenting the human aortic aneurysm in CT angiograms (CTA), which, in turn, allows measurements of volume and morphological aspects useful for treatment planning. The system estimates a rough "initial surface," and then refines it using a level set segmentation scheme augmented with two external analyzers: The global region analyzer, which incorporates a priori knowledge of the intensity, volume, and shape of the aorta and other structures, and the local feature analyzer, which uses voxel location, intensity, and texture features to train and drive a support vector machine classifier. Each analyzer outputs a value that corresponds to the likelihood that a given voxel is part of the aneurysm, which is used during level set iteration to control the evolution of the surface. We tested our system using a database of 20 CTA scans of patients with aortic aneurysms. The mean and worst case values of volume overlap, volume error, mean distance error, and maximum distance error relative to human tracing were 95.3% +/- 1.4% (s.d.); worst case = 92.9%, 3.5% +/- 2.5% (s.d.); worst case = 7.0%, 0.6 +/- 0.2 mm (s.d.); worst case = 1.0 mm, and 5.2 +/- 2.3 mm (s.d.); worst case = 9.6 mm, respectively. When implemented on a 2.8 GHz Pentium IV personal computer, the mean time required for segmentation was 7.4 +/- 3.6 min (s.d.). We also performed experiments that suggest that our method is insensitive to parameter changes within 10% of their experimentally determined values. This preliminary study proves feasibility for an accurate, precise, and robust system for segmentation of the abdominal aneurysm from CTA data, and may be of benefit to patients with aortic aneurysms.
Authors:
Feng Zhuge; Geoffrey D Rubin; Shaohua Sun; Sandy Napel
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, N.I.H., Extramural; Validation Studies    
Journal Detail:
Title:  Medical physics     Volume:  33     ISSN:  0094-2405     ISO Abbreviation:  Med Phys     Publication Date:  2006 May 
Date Detail:
Created Date:  2006-06-06     Completed Date:  2006-07-17     Revised Date:  2010-06-15    
Medline Journal Info:
Nlm Unique ID:  0425746     Medline TA:  Med Phys     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1440-53     Citation Subset:  IM    
Affiliation:
Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA. zhugef@stanford.edu
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MeSH Terms
Descriptor/Qualifier:
Aged
Algorithms*
Aortic Aneurysm, Abdominal / radiography*
Aortography / methods*
Artificial Intelligence
Female
Humans
Imaging, Three-Dimensional / methods*
Male
Middle Aged
Pattern Recognition, Automated / methods
Radiographic Image Enhancement / methods*
Radiographic Image Interpretation, Computer-Assisted / methods*
Retrospective Studies
Tomography, X-Ray Computed / methods*
Grant Support
ID/Acronym/Agency:
R01 HL058915-01A2/HL/NHLBI NIH HHS; R01 HL058915-02/HL/NHLBI NIH HHS; R01 HL058915-03/HL/NHLBI NIH HHS; R01 HL058915-04/HL/NHLBI NIH HHS; R01 HL58915/HL/NHLBI NIH HHS; R01 HL67194/HL/NHLBI NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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