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Proceedings Paper

User-assisted aortic aneurysm analysis
Author(s): Amandine Ouvrard; Rahul Renapuraar; Randolph M. Setser; Scott Flamm; Thomas O'Donnell
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Paper Abstract

Aortic Aneurysms (AA) are the 13th leading cause of death in the US. In standard clinical practice, intervention is initiated when the maximal diameter cross-sectional reaches 5.5cm. However, this is a 1D measure and it has been suggested in the literature that higher order measurements (area, volume) might be more appropriate clinically. Unfortunately, no commercially available tools exist for extracting a 3D model of the epithelial layer (versus the lumen) of the vessel. Therefore, we present work towards semi-automatically recovering the aorta from CT angiography volumes with the aim to facilitate such studies. We build our work upon a previous approach to this problem. Bodur et. al., presented a variant of the iso-perimetric algorithm to semi-automatically segment several individual aortic cross-sections across longitudinal studies, quantifying any growth. As a by-product of these sparse cross-sections, it is possible to form a series of rough 3D models of the aorta. In this work we focus on creating a more detailed 3D model at a single time point by automatically recovering the aorta between the sparse user-initiated segmentations. Briefly, we fit a tube model to the sparse segmentations to approximate the cross-sections at intermediate regions, refine the approximations and apply the isoperimetric algorithm to them. From these resulting dense cross-sections we reconstruct our model. We applied our technique to 12 clinical datasets which included significant amounts of thrombus. Comparisons of the automatically recovered cross-sections with cross-sections drawn by an expert resulted in an average difference of .3cm for diameter and 2cm^2 for area.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72593Q (27 March 2009); doi: 10.1117/12.812522
Show Author Affiliations
Amandine Ouvrard, Siemens Corporate Research (United States)
Rahul Renapuraar, The Cleveland Clinic Foundation (United States)
Randolph M. Setser, The Cleveland Clinic Foundation (United States)
Scott Flamm, The Cleveland Clinic Foundation (United States)
Thomas O'Donnell, Siemens Corporate Research (United States)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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