Automated Segmentation of Atherosclerotic Plaque in MDCT Angiogaphy of the Carotid Artery

There is large interest in automated carotid artery plaque analysis, as there is increasing evidence that atherosclerotic plaque volume and composition are related to acute cerebrovascular events. In this study, a novel semi-automatic method was developed to segment the plaque in the common carotid artery. First the carotid lumen is semi-automatically segmented using a level set approach initialized with three seed points [1]. Then calcified regions located within the vessel are automatically detected with the GentleBoost framework using a number of descriptive features. In the next step, voxels are classified as being inside or outside vessel with the GentleBoost framework with a similar set of features [2]. Finally, a 2D ellipse shape deformable model is fitted to the combined result of the calcium and vessel wall classification. The ellipses are fitted in a way that automatically detected calcified regions are positioned inside each ellipse, at the same time the sum of intensities along the ellipse is minimized. The method was trained on 40 CTA datasets, and tested on 60 data sets. For 20 datasets from the test set, second observer manual segmentations were available. The evaluation included comparison between the automated and manual segmentation and a comparison with interobserver variability. Automated segmentation revealed a vessel volume of 897A-A?A 1/2 722mm3, while difference between manual and automated segmentation was 3.1A-A?A 1/2 116 mm3 (p=0.92), the similarity index (SI) was 91% and the coefficient of variation (CoV) is 13%. This was better than the interobserver variability with a difference of -20A-A?A 1/2 118 mm3 (p=0.46), an SI of 92% and a CoV of 15%. Automated segmentation revealed a plaque volume (vessel volumeA-A?A 1/2 lumen volume) of 393A-A?A 1/2 295 mm3, while difference between manual and automated segmentation was -4.7A-A?A 1/2 116 mm3 (p=0.75), the SI was 78% and the CoV is 29%. This was better than the interobserver variability with a difference of -37A-A?A 1/2 121 mm3 (p=0.19), an SI of 80% and a CoV of 33%. We conclude that automated segmentation of the atherosclerotic plaque is robust and accurate when compared to the manual interobserver variability. REFERENCES [1]Manniesing R, Viergever MA, Niessen WJ. A-A?A 1/2 Vessel Axis Tracking Using Topology Constrained Surface Evolution,?? IEEE Transaction on Medical Imaging, 26(3), 309-316 (2007) [2]J.Friedman, T.Hastie, R. Tibishirani, A-A?A 1/2 Additive Logistic Regression: A Statistical View of Boosting??, The Annals of Statistics, vol. 28, pp.337-407, 2000