Skip to main content

An abdominal aortic aneurysm segmentation method: level set with region and statistical information.

Publication ,  Journal Article
Zhuge, F; Rubin, GD; Sun, S; Napel, S
Published in: Med Phys
May 2006

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.

Duke Scholars

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

May 2006

Volume

33

Issue

5

Start / End Page

1440 / 1453

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Retrospective Studies
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiographic Image Enhancement
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Middle Aged
  • Male
  • Imaging, Three-Dimensional
  • Humans
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Zhuge, F., Rubin, G. D., Sun, S., & Napel, S. (2006). An abdominal aortic aneurysm segmentation method: level set with region and statistical information. Med Phys, 33(5), 1440–1453. https://doi.org/10.1118/1.2193247
Zhuge, Feng, Geoffrey D. Rubin, Shaohua Sun, and Sandy Napel. “An abdominal aortic aneurysm segmentation method: level set with region and statistical information.Med Phys 33, no. 5 (May 2006): 1440–53. https://doi.org/10.1118/1.2193247.
Zhuge F, Rubin GD, Sun S, Napel S. An abdominal aortic aneurysm segmentation method: level set with region and statistical information. Med Phys. 2006 May;33(5):1440–53.
Zhuge, Feng, et al. “An abdominal aortic aneurysm segmentation method: level set with region and statistical information.Med Phys, vol. 33, no. 5, May 2006, pp. 1440–53. Pubmed, doi:10.1118/1.2193247.
Zhuge F, Rubin GD, Sun S, Napel S. An abdominal aortic aneurysm segmentation method: level set with region and statistical information. Med Phys. 2006 May;33(5):1440–1453.

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

May 2006

Volume

33

Issue

5

Start / End Page

1440 / 1453

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Retrospective Studies
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiographic Image Enhancement
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Middle Aged
  • Male
  • Imaging, Three-Dimensional
  • Humans