Ms. Emily L. Spangler Profile

Emily L. Spangler

Medical Student

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Proceedings Article | 8 March 2007 Paper

Evaluation of internal carotid artery segmentation by InsightSNAP

Emily Spangler, Christopher Brown, John Roberts, Brian Chapman

Proceedings Volume 6512, 65123F (2007) https://doi.org/10.1117/12.709954

KEYWORDS: Image segmentation, Arteries, Blood, 3D image processing, 3D-TOF imaging, Independent component analysis, Magnetic resonance imaging, Visualization, Angiography, 3D acquisition

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Quantification of cervical carotid geometry may facilitate improved clinical decision making and scientific discovery. We set out to evaluate the ability of InsightSNAP (ITK-SNAP), an open-source segmentation program for 3D medical images (http://www.itksnap.org, version 1.4), to semi-automatically segment internal carotid arteries. A sample of five individuals (three normal volunteers, and two diseased patients) were imaged with an MR exam consisting of a MOTSA TOF MRA image volume and multiple black blood images acquired with different contrast weightings. Comparisons were made to a manual segmentation created during simultaneous evaluation of the MOTSA image and the various black blood images (typically PD-weighted, T1-weighted, and T2-weighted). These individuals were selected as a training set to determine acceptable parameters for ITK-SNAP's semi-automatic level sets segmentation method. The conclusion from this training set was that the initial thresholding (assigning probabilities to the intensities of image pixels) in the image pre-processing step was most important to obtaining an acceptable segmentation. Unfortunately no consistent trends emerged in how this threshold should be chosen. Figures of percent over- and under-segmentation were computed as a means of comparing the hand segmented and semi-automatically segmented internal carotids. Overall the under-segmentation by ITK-SNAP (voxels included in the manual segmentation but not in the semiautomated segmentation) was 10.94% ± 6.35% while the over-segmentation (voxels excluded in the manual segmentation but included in the semi-automated segmentation) was 8.16% ± 4.40% defined by reference to the total number of voxels included in the manual segmentation.

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