Does an automatic computer-based tracing algorithm accurately segment the common carotid artery in longitudinal B-mode ultrasound images across different scanners and pathologies?
The proposed automatic computer-based tracing algorithm robustly segments the common carotid artery in ultrasound images regardless of the scanner used or the presence of pathology.
Objective. The aim of this paper is to show an algorithm for the automatic computer-based tracing (ACT) of common carotid artery (CCA) in longitudinal B-mode ultrasound images characterized by four main features: (i) user-independence; (ii) suitability to normal and pathological images; (iii) robustness to noise; and (iv) independent of ultrasound OEM scanner. Methods. Three hundred longitudinal B-mode images (100 normal CCAs, 100 CCAs with increased intima-media thickness, 60 stable plaques, and 40 echolucent plaques) were acquired using three different (GE, Siemens, and Biosound) OEM ultrasound image scanners. The algorithm processed each image to delineate the region of interest containing the CCA. Output of the algorithm are three segmentation lines representing (a) distal (far) and (b) near adventitia layers, and (c) lumen of the CCA. Three operators qualitatively scored the ACTs. Results. The CCA was correctly automatically traced in all the 300 B-mode images. The performance was independent on the image scanner used to acquire the image or on the type of the CCA (healthy versus pathologic). Eight ACTs out of 300 received a poor score after visual inspection due to an automated adventitia tracing that did not correctly follow the CCA wall in a small portion of the image. Conclusions. The proposed algorithm is robust in ACTs of CCA since it is independent of scanner and normal/abnormal wall. This approach could constitute a general basis for a completely automated segmentation procedure.
Molinari et al. (Tue,) studied this question.