Paper
9 March 2010 An hybrid CPU-GPU framework for quantitative follow-up of abdominal aortic aneurysm volume by CT angiography
Claude Kauffmann, An Tang, Eric Therasse, Gilles Soulez
Author Affiliations +
Abstract
We developed a hybrid CPU-GPU framework enabling semi-automated segmentation of abdominal aortic aneurysm (AAA) on Computed Tomography Angiography (CTA) examinations. AAA maximal diameter (D-max) and volume measurements and their progression between 2 examinations can be generated by this software improving patient followup. In order to improve the workflow efficiency some segmentation tasks were implemented and executed on the graphics processing unit (GPU). A GPU based algorithm is used to automatically segment the lumen of the aneurysm within short computing time. In a second step, the user interacted with the software to validate the boundaries of the intra-luminal thrombus (ILT) on GPU-based curved image reformation. Automatic computation of D-max and volume were performed on the 3D AAA model. Clinical validation was conducted on 34 patients having 2 consecutive MDCT examinations within a minimum interval of 6 months. The AAA segmentation was performed twice by a experienced radiologist (reference standard) and once by 3 unsupervised technologists on all 68 MDCT. The ICC for intra-observer reproducibility was 0.992 (≥0.987) for D-max and 0.998 (≥0.994) for volume measurement. The ICC for inter-observer reproducibility was 0.985 (0.977-0.90) for D-max and 0.998 (0.996- 0.999) for volume measurement. Semi-automated AAA segmentation for volume follow-up was more than twice as sensitive than D-max follow-up, while providing an equivalent reproducibility.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Claude Kauffmann, An Tang, Eric Therasse, and Gilles Soulez "An hybrid CPU-GPU framework for quantitative follow-up of abdominal aortic aneurysm volume by CT angiography", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76240N (9 March 2010); https://doi.org/10.1117/12.844499
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Cited by 2 scholarly publications.
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KEYWORDS
Image segmentation

3D modeling

3D acquisition

Angiography

Image processing algorithms and systems

Computed tomography

3D image processing

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