A Semi-automated image segmentation approach for computational fluid dynamics studies of aortic dissection

Jeff R. Anderson, Christof Karmonik, Yannick Georg, Jean Bismuth, Alan B. Lumsden, Adeline Schwein, Mickael Ohana, Fabien Thaveau, Nabil Chakfe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Computational studies of aortic hemodynamics require accurate and reproducible segmentation of the aortic tree from whole body, contrast enhanced CT images. Three methods were vetted for segmentation. A semi-automated approach that utilizes denoising, the extended maxima transform, and a minimal amount of manual segmentation was adopted.

Original languageEnglish (US)
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4727-4730
Number of pages4
ISBN (Electronic)9781424479290
DOIs
StatePublished - Nov 2 2014
Event2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
Duration: Aug 26 2014Aug 30 2014

Publication series

Name2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

Other

Other2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
CountryUnited States
CityChicago
Period8/26/148/30/14

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering
  • Medicine(all)

Fingerprint Dive into the research topics of 'A Semi-automated image segmentation approach for computational fluid dynamics studies of aortic dissection'. Together they form a unique fingerprint.

Cite this