A completely user-independent algorithm for carotid artery segmentation performed similarly to trained operators, achieving a segmentation error of less than 1 pixel for both key interfaces.
Does a completely user-independent algorithm accurately segment the carotid artery wall in 2-D ultrasound images compared to expert manual segmentation?
A novel user-independent algorithm for carotid artery ultrasound segmentation performs comparably to expert manual tracing with sub-pixel error rates.
The analysis of the carotid artery wall is crucial for the diagnosis of serious cardiovascular pathologies or for the assessment of a subject's cardiovascular risk. Several algorithms have been proposed for the segmentation of ultrasound carotid artery images, but almost all require a certain degree of user interaction. We recently developed a completely user-independent algorithm for the segmentation of the common-carotid-artery wall; specifically, the algorithm traces the contour of the interfaces between the lumen and the intima layer and between the media and adventitia layers. In this paper, we show the characterization of the algorithm in terms of segmentation error. Moreover, we compare the output of the algorithm with the segmentations manually traced by four experts, using the percent statistics test and testing the automatically generated segmentation against the average human segmentations. We show that our algorithm's segmentation is not statistically different from that of a trained operator and that the segmentation error is lower than 1 pixel for both the lumen-intima interface and for the media-adventitia interface.
Delsanto et al. (Wed,) conducted a other in Carotid artery segmentation. User-independent algorithm for carotid artery segmentation vs. Manual segmentation by experts was evaluated on Segmentation error. A completely user-independent algorithm for carotid artery segmentation performed similarly to trained operators, achieving a segmentation error of less than 1 pixel for both key interfaces.