A semiautomated segmentation algorithm for 3D ultrasound of the carotid artery yielded high accuracy and reduced operator time (2.8 min vs 8.3 min) compared to manual methods.
Does a semiautomated segmentation algorithm improve the efficiency and maintain the accuracy of carotid artery vessel wall volume measurement compared to manual segmentation in 3D ultrasound images?
A novel semiautomated level set-based segmentation algorithm for 3D carotid ultrasound significantly reduces operator time while maintaining high accuracy and precision comparable to manual segmentation.
PURPOSE: Three-dimensional ultrasound (3D US) of the carotid artery provides measurements of arterial wall and plaque vessel wall volume (VWV) that are complementary to the one-dimensional measurement of the carotid artery intima-media thickness. 3D US VWV requires an observer to delineate the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) of the carotid artery. The main purpose of this work was to develop and evaluate a semiautomated segmentation algorithm for delineating the MAB and LIB of the carotid artery from 3D US images. METHODS: To segment the MAB and LIB, the authors used a level set method and combined several low-level image cues with high-level domain knowledge and limited user interaction. First, the operator initialized the algorithm by choosing anchor points on the boundaries, identified in the images. The MAB was segmented using local region- and edge-based energies and an energy that encourages the boundary to pass through anchor points from the preprocessed images. For the LIB segmentation, the authors used local and global region-based energies, the anchor point-based energy, as well as a constraint promoting a boundary separation between the MAB and LIB. The data set consisted of 231 2D images (11 2D images per each of 21 subjects) extracted from 3D US images. The image slices were segmented five times each by a single observer using the algorithm and the manual method. Volume-based, region-based, and boundary distance-based metrics were used to evaluate accuracy. Moreover, repeated measures analysis was used to evaluate precision. RESULTS: The algorithm yielded an absolute VWV difference of 5.0% +/- 4.3% with a segmentation bias of -0.9% +/- 6.6%. For the MAB and LIB segmentations, the method gave absolute volume differences of 2.5% +/- 1.8% and 5.6% +/- 3.0%, Dice coefficients of 95.4% +/- 1.6% and 93.1% +/- 3.1%, mean absolute distances of 0.2 +/- 0.1 and 0.2 +/- 0.1 mm, and maximum absolute distances of 0.6 +/- 0.3 and 0.7 +/- 0.6 mm, respectively. The coefficients of variation of the algorithm (5.1%) and manual methods (3.9%) were not significantly different, but the average time saved using the algorithm (2.8 min versus 8.3 min) was substantial. CONCLUSIONS: The authors generated and tested a semiautomated carotid artery VWV measurement tool to provide measurements with reduced operator time and interaction, with high Dice coefficients, and with necessary required precision.
Ukwatta et al. (Fri,) conducted a other in Carotid atherosclerosis (n=21). Semiautomated segmentation algorithm vs. Manual segmentation method was evaluated on Accuracy (absolute volume differences, Dice coefficients, distances) and precision. A semiautomated segmentation algorithm for 3D ultrasound of the carotid artery yielded high accuracy and reduced operator time (2.8 min vs 8.3 min) compared to manual methods.