4.3 Challenges in image-based methods
Thus, conventional segmentation methods are either semi-automatic or automatic. Human intervention is required at many stages, for example, ROI selection, seed point and/or contour initialization, and distance evaluation [122]. This leads to additional latency and is often error prone. Further, the curvature of the artery and its orientation impinge on segmentation [122]. Most segmentation algorithms require arteries to be recorded horizontally. Spatial transformations are applied to improve orientation before segmentation [122]. Furthermore, the presence of plaque, plaque irregularity and mimicking arterial structures such as jugular vein complicates the process. Moreover, scanner variability, operator experience, angle of incidence (of ultrasound probe), blood back-scattering, shadowing and ultrasound artefacts add to the conundrum [123]. Application of machine learning and deep learning algorithms provided a competitive edge in this scenario.