4. 1 Segmentation of lumen region and assessment of lumen
diameter
Lumen is the area between the lumen-intima (LI) boundary of the
near-wall (LI-near) and far-wall (LI-far). The distance between LI-near
and LI-far is defined as lumen diameter (LD) whereas the gap between
intima and media is labelled as IMT. Figure 3. shows a longitudinal
segment of the common carotid artery (CCA) and the delineation of LI and
MA boundaries. Characterization of the lumen is primary for the
assessment of LD and IMT. However, lumen recognition is challenging
because of variability in dataset, plaque composition-and-morphology,
arterial structure, presence of stenosis, jugular vein and importantly
the imaging standards [96,97]. Algorithms applied several theories
to overcome these challenges. The most
prominent assumptions are that (a) the brightest boundary corresponds to
media-adventitia (MA) edge, and (b) the arterial blood flow is laminar
[88]. These theories aid in identifying the edges and ROI. In
previous studies of Sifakis et al. [98], statistical estimates were
used for automatic recognition of lumen, both in multi-frame and single
frame. The success rate was 99% and the algorithm was effective even in
the presence of plaques and moderate amounts of “mimicking arteries”
[98]. Surprisingly, the algorithm failed in images with poor
far-wall representation and sharp arterial curvature [98]. Over the
years, an enormous amount of work was done on automated lumen
characterization. Araki et al. [88] used automated ROI detection,
spectral analysis and K-means classifier for lumen characterization. The
algorithm performed well (Dice similarity ~1) but was
susceptible to noise, motion artefacts and presence of plaque. Molinari
et al. [99] suggested a novel approach for seed point selection
followed by curve fitting and classification. The method was successful
in 92% cases except in the presence of backscattering and plaque
[99]. Most algorithms in the past preferred straight vessels in the
frame for better segmentation. Kumar et al. [100] applied spatial
transformation for pre-processing (straightening curved vessels) and
scale-space for boundary segmentation. Dice similarity and Jaccard index
were 94.2, 89.1and 93.9, 88.6 respectively on two different sets of GT.
Carvalho et al. [95] used contrast-enhanced ultrasound along with
the B-mode ultrasound to overcome motion artefacts and noise. Further,
joint-histogram and graph-based methods were employed for segmentation.
The authors informed segmentation error as the average root mean square
error (RMSE) of 112±73 µm compared with the expert recording on two
datasets [95]. Rocha et al. [101] used a linear-Bayes classifier
along with dynamic programming (DP) for segmentation on 199 images. The
authors reported a success rate of 99.5% and robustness to shadowing,
scanner variability and plaque irregularity [101]. Further, reviews
on a comparison between different segmentation approaches are presented
elsewhere [102]. Table I. summarizes benchmarking studies on lumen
segmentation and LD assessment.
Table I. Benchmarking studies on lumen segmentation and LD assessment.