Automatic detection of supraaortic branches and model-based segmentation of the aortic arch froM 3D CTA images

Automated quantification of the morphology of the aortic arch is crucial for diagnosis and treatment of cardiovascular diseases. We introduce a new approach for fully automatic segmentation and characterization of the aortic arch morphology for endovascular aortic repair. Supraaortic branches are de...

Full description

Saved in:
Bibliographic Details
Published in2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 486 - 489
Main Authors Biesdorf, A., Worz, S., von Tengg-Kobligk, H., Rohr, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Angiography
Aortic Arch Segmentation
Automatic Initialization
Bioinformatics
Biomedical imaging
Branch Detection
Cancer
Cardiovascular diseases
Computer vision
Deformable models
Genomics
Image segmentation
Model-Based Segmentation
Morphology
Online AccessGet full text

Cover

More Information
Summary:Automated quantification of the morphology of the aortic arch is crucial for diagnosis and treatment of cardiovascular diseases. We introduce a new approach for fully automatic segmentation and characterization of the aortic arch morphology for endovascular aortic repair. Supraaortic branches are detected based on an analysis of the connected components within a spherical volume around the vessel. Segmentation and quantification is based on a 3D parametric intensity model that is iteratively fitted to the image intensities and includes a fast and robust scheme for initialization. The performance of the approach has been evaluated using synthetic and real 3D CTA images.
ISBN:1424439310
9781424439317
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2009.5193090