11 March 2008 Fully automated segmentation of carotid and vertebral arteries from contrast enhanced CTA
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Abstract
We propose a method for segmenting and labeling the main head and neck vessels (common, internal, external carotid, vertebral) from a contrast enhanced computed tomography angiography (CTA) volume. First, an initial centerline of each vessel is extracted. Next, the vessels are segmented using 3D active objects initialized using the first step. Finally, the true centerline is identified by smoothly deforming it away from the segmented mask edges using a spline-snake. We focus particularly on the novel initial centerline extraction technique. It uses a locally adaptive front propagation algorithm that attempts to find the optimal path connecting the ends of the vessel, typically from the lowest image of the scan to the Circle of Willis in the brain. It uses a patient adapted anatomical model of the different vessels both to initialize and constrain this fast marching, thus eliminating the need for manual selection of seed points. The method is evaluated using data from multiple regions (USA, India, China, Israel) including a variety of scanners (10, 16, 40, 64-slice; Brilliance CT, Philips Healthcare, Cleveland, OH, USA), contrast agent dose, and image resolution. It is fully successful in over 90% of patients and only misses a single vessel in most remaining cases. We also demonstrate its robustness to metal and dental artifacts and anatomical variability. Total processing time is approximately two minutes with no user interaction, which dramatically improves the workflow over existing clinical software. It also reduces patient dose exposure by obviating the need to acquire an unenhanced scan for bone suppression as this can be done by applying the segmentation masks.
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Olivier Cuisenaire, Olivier Cuisenaire, Sunny Virmani, Sunny Virmani, Mark E. Olszewski, Mark E. Olszewski, Roberto Ardon, Roberto Ardon, } "Fully automated segmentation of carotid and vertebral arteries from contrast enhanced CTA", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69143R (11 March 2008); doi: 10.1117/12.770481; https://doi.org/10.1117/12.770481
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