2 research outputs found
Quantifying atherosclerosis in vasculature using ultrasound imaging
Cerebrovascular disease accounts for approximately 30% of the global burden
associated with cardiovascular diseases [1]. According to the World Stroke
Organisation, there are approximately 13.7 million new stroke cases annually,
and just under six million people will die from stroke each year [2]. The
underlying cause of this disease is atherosclerosis – a vascular pathology
which is characterised by thickening and hardening of blood vessel walls.
When fatty substances such as cholesterol accumulate on the inner linings of
an artery, they cause a progressive narrowing of the lumen referred to as a
stenosis.
Localisation and grading of the severity of a stenosis, is important for
practitioners to assess the risk of rupture which leads to stroke. Ultrasound
imaging is popular for this purpose. It is low cost, non-invasive, and permits a
quick assessment of vessel geometry and stenosis by measuring the intima
media thickness. Research is showing that 3D monitoring of plaque
progression may provide a better indication of sites which are at risk of
rupture. Various metrics have been proposed. From these, the quantification
of plaques by measuring vessel wall volume (VWV) using the segmented
media-adventitia boundaries (MAB) and lumen-intima boundaries (LIB) has
been shown to be sensitive to temporal changes in carotid plaque burden.
Thus, methods to segment these boundaries are required to help generate
VWV measurements with high accuracy, less user interaction and increased
robustness to variability in di↵erent user acquisition protocols.ii
This work proposes three novel methods to address these requirements, to
ultimately produce a highly accurate, fully automated segmentation algorithm
which works on intensity-invariant data. The first method proposed was that
of generating a novel, intensity-invariant representation of ultrasound data by
creating phase-congruency maps from raw unprocessed radio-frequency
ultrasound information. Experiments carried out showed that this
representation retained the necessary anatomical structural information to
facilitate segmentation, while concurrently being invariant to changes in
amplitude from the user. The second method proposed was the novel
application of Deep Convolutional Networks (DCN) to carotid ultrasound
images to achieve fully automatic delineation of the MAB boundaries, in
addition to the use of a novel fusion of amplitude and phase congruency data
as an image source. Experiments carried out showed that the DCN produces
highly accurate and automated results, and that the fusion of amplitude and
phase yield superior results to either one alone. The third method proposed
was a new geometrically constrained objective function for the network's
Stochastic Gradient Descent optimisation, thus tuning it to the segmentation
problem at hand, while also developing the network further to concurrently
delineate both the MAB and LIB to produce vessel wall contours. Experiments
carried out here also show that the novel geometric constraints improve the
segmentation results on both MAB and LIB contours.
In conclusion, the presented work provides significant novel contributions to
field of Carotid Ultrasound segmentation, and with future work, this could lead
to implementations which facilitate plaque progression analysis for the end�user