Study Method Dataset & size Measures Findings
Sifakis et al. [98]
Vertical intensity profile based signal selection, segment filtering, Statistical methods for lumen centre point identification
N = 2149 (100)
SR =100% Mean FC = 95.76±9.61% Error = 0.43±0.26 mm
CCA recognition in: poor image quality, presence of plaque, jugular vein, intensity variations and moderate arterial curvature
Araki et al. [88]
Spectral analysis for peak detection, K-means classifier
N = 404 (202)
DS ~1, JI=92.1 Mann-Whitney U= 63,356.5 Mean error, PoM Cross-correlation SSI
Segmented result close to GT. Consistent LD assessment. Automated and manual results are consistent if SSI>40%. Susceptible to noise, motion artefacts and noise.
Molinari et al. [99]
Geometric feature extraction, Line fitting, classification
N = 200
mean distance errors ± SD in near-wall = 1.05 ± 1.04 pixels in far wall 2.68 ± 3.94 pixels
Automatic delineation of CCA.
Kumar et al. [100]
Vertical spectral analysis to trace adventitial border Spatial transformation to straighten curved vessels. Scale-space segmentation. N = 404 (202) DS and JI were 94.2 (89.1) and 93.9 (88.6). Mean LD error = 0.27±0.25 mm Mean IAD error = 0.24±0.24 mm
Delineation lumen and adventitial borders even in curved vessels. Validated with expert readings
Carvalho et al. [95]
Centreline estimation, Graph-based segmentation using DP. N = 21 (17): 2 datasets of size 11 and 10
Dataset 1(RMSE = 191±43 µm) and Dataset 2 (RMSE 351 ± 176 µm) carotids.
Both contrast enhanced-US and B-mode were used for lumen segmentation.
Rocha et al. [101]
Auto ROI, DP to delineate longitudinal paths, Linear Bayes classifier.
N=199 Two datasets
SR=99.5%
Real-time processing, fully automated.