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Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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IOP Publishing
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Vascular and Intravascular Imaging Trends, Analysis, and
Challenges, Volume 1
Stent applications
Petia Radeva and Jasjit S Suri
Chapter 13
Stroke risk stratification and its validation using
ultrasonic echolucent carotid wall plaque
morphology: a machine learning paradigm
Tadashi Araki, Pankaj K Jain, Harman S Suri, Narendra D Londhe, Nobutaka
Ikeda, Ayman El-Baz, Vimal K Shrivastava, Luca Saba, Andrew Nicolaides, Shoaib
Shafique, John R Laird, Ajay Gupta and Jasjit S Suri
Stroke risk stratification based on grayscale morphology of the ultrasound carotid
wall has recently been shown to have promise in the classification of high-risk versus
low-risk plaques or symptomatic versus asymptomatic plaques. In previous studies,
this stratification has been mainly based on analysis of the far wall of the carotid
artery. Due to the multifocal nature of atherosclerotic disease, plaque growth is not
restricted to the far wall alone. This chapter presents a new approach for stroke risk
assessment by integrating assessment of both the near and far walls of the carotid
artery using grayscale morphology of the plaque. Further, this chapter presents a
scientific validation system for stroke risk assessment. Both these innovations have
never been presented before.
The methodology consists of an automated segmentation system of the near wall
and far wall regions in grayscale carotid B-mode ultrasound scans. Sixteen grayscale
texture features are computed and fed into the machine learning system. The
training system utilizes the lumen diameter to create ground truth labels for the
stratification of stroke risk. A cross-validation procedure is adapted in order to
obtain the machine learning testing classification accuracy through the use of three
sets of partition protocols: K5, K10 and Jack Knife.
The mean classification accuracies over all sets of partition protocols for the
automated system in the far and near walls are 95.08% and 93.47%, respectively.
The corresponding accuracies for the manual system are 94.06% and 92.02%,
respectively. The precisions-of-merit (PoMs) of the automated machine learning
doi:10.1088/2053-2563/ab01fach13 13-1 ª IOP Publishing Ltd 2019
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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system compared to the manual risk assessment system are 98.05% and 97.53% for
the far and near walls, respectively. The receiver operating characteristic (ROC) of
the risk assessment system for the far and near walls is close to 1.0, demonstrating
high accuracy.
13.1 Introduction
Stroke is the fifth leading cause of death in the United States. On average, someone
in the United States has a stroke every 40 s [1]. The WHO estimates that these
cerebrovascular accidents (CVA), or strokes, account for the loss of 6.7 million lives
per year [2]. One of the leading causes of these strokes is carotid artery disease
(CAD) [3–5], which occurs when the carotid arteries become blocked (so called
‘stenosis’). When carotid artery stenosis occurs, there is a risk that oxygenated blood
may not be available to the brain because of either reduced perfusion pressure from
the narrowed carotid artery or because of a rupture plaque that blocks a downstream blood vessel in the brain. This stenosis of the carotid arteries, as depicted in
figure 13.1, is most commonly caused by atherosclerosis [6]. Atherosclerosis is
caused by to the accumulation of fatty deposits known as plaque along the
innermost layer of the arteries (causing stenosis), where blood normally fl ows.
13.1.1 Small changes in the wall leading to cIMT
The biology of atherosclerotic disease leads to the formation of different plaque
components in the carotid arterial wall over time [6]. An atherosclerotic plaque has
Figure 13.1. Carotid anatomy (left) and carotid atherosclerotic plaque formation in the near and far walls
(right).
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multiple components such as plaque hemorrhage (PH), thrombus (T), lipids,
necrotic cap thickness (NCT), intima thickness, calcium, fibrosis cap (FC) and
smooth muscle cells (SMCs) [7, 8]. There are two biological changes emerging out of
this formation: (a) small changes in the intima and media walls [9] and (b) aggressive
changes in the arterial wall leading to stenosis [10]. These small changes in the walls
of the carotid artery bring an increase in thickness, which is measured as the carotid
intima–media thickness (cIMT). Scientists have used cIMT as a biomarker for
predicting the occurrence of major adverse cardiovascular events [11, 12]. Several
studies have shown a relationship between varying cIMT thresholds and cardiovascular disease (CVD) [13–15]: (cIMT > 0.8 mm) [10], (cIMT > 0.9 mm) [13, 14],
(cIMT > 1.1 mm) [16], (cIMT> 1.15 mm) [17] and (cIMT > 1.26 mm) [18].
13.1.2 The role of the lumen diameter
Multifocal and aggressive changes in the arterial wall cause a drastic change in the
lumen diameter (LD), and are referred to as stenosis. Previous research [12, 19] has
shown a link between LD and CVE. In 2011, Polak et al [20] hypothesized that an
increase in the internal diameter of the common carotid artery (CCA), also known
as the LD, is associated with age, gender and echocardiographically estimated left
ventricular (LV) mass. Recent studies have shown that the carotid arterial diameters
also have a better predictive power for CAD [11, 12, 21–23]. We infer that LD offers
a method for characterization of high and low stroke risk [24–27]. It is important to
note here that these measurements are of great value and must be calculated without
subjectivity, and further can be utilized as a building block for risk assessment based
on machine learning.
13.1.3 The role of grayscale morphological-based tissue characterization
As plaque matures with age in a carotid artery, the number of plaque components
increases in the plaque [28]. This is shown to change the echolucency in ultrasound
scans [29]. These plaques can be symptomatic or asymptomatic [30–32]. In general,
studies have shown that symptomatic plaques may be predominantly hypo-echoic in
nature, while asymptomatic plaques are less bright and relatively hyper-echoic,
although there is significant variability across individual patients [29]. Due to the
multifocal nature of the disease, it has been seen that hypo-echoic plaque regions can
be surrounded by hyper-echoic regions [33]. It is therefore challenging to characterize the plaque visually and thus it is necessary to have a morphological-based tissue
characterization protocol for stroke risk assessment. We assume that the plaque
components in ultrasound scans can be used to assess the risk based on tissue
morphology, along with the carotid LD measurements, which can act as a label for
high or low risk. This can be accomplished using machine learning and this study
adapts such a model. In order to classify the risk posed by different levels of plaque
build-up in the carotid artery, a technique of tissue characterization is used which
qualitatively analyzes the different statistical features that compose the plaque in the
carotid artery [34–38]. Note that the risk assessment based on LD alone is not
sufficient. This is because the grayscale information corresponding to different
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plaques (such as lipids, macrophages, fibro fatty tissue and calcium) in the wall
region is not utilized during the risk assessment [39–41].
13.1.4 The importance of near wall and tissue characterization
The study of ultrasound tissue characterization relies on the results from B-mode
ultrasound [9] in order to characterize the difference between high- and low-risk
patients based on the values of statistical features [42]. This process is repeated in the
near wall, far wall and combined wall of the carotid artery in order to determine a
holistic method of risk assessment, while at the same time comparing the error
obtained from two different places of interaction within an ultrasound image. This is
of unique value because the near wall of the carotid artery is thought to be of little
historical importance [14] in risk assessment and is thus the main contribution of this
study. The reason for this is the low intensity contained in ultrasound images
corresponding to the near wall. However, as there is an equal likelihood for the
development of plaque build-up on this side of the carotid artery, this current study
aims to develop a machine learning based stroke risk assessment system (sRAS) so
that the visual (manual) error from the low intensity of the near wall does not affect
the reliability of the overall results.
13.1.5 A sRAS for the near and far walls using a machine learning paradigm
The machine learning approach [39] adapted in this study aims to provide a more
comprehensive solution to the problems in manual risk assessment, in particular when
the combined grayscale wall (near and far) of the carotid artery ultrasound scan is taken
into consideration. By first segmenting the desired wall region in ultrasound scans, then
extracting its grayscale features along with measurements of LD, we were able to train
the machine learning system and obtain the high- and low-risk coefficients [43]. This
information was then given to the system along with the test segmented wall region and
its corresponding grayscale statistical features [44] to categorize the carotid disease risk
into low-risk or high-risk categories. This process was done for K = 5 partitions to begin
with, where the system would separate 80% of the patient sample size for learning and
20% for testing. In the testing phase, when the input of high- or low-risk is not given to
the system and using the information it has learned from the 80% of the data along with
the segmented grayscale statistical features from the remaining 20%, we can predict a
decision of high- or low-risk carotid plaque. Similarly, this was done for K = 10 (where
90% of the data were used for learning and 10% for testing) and K = N (Jack Knife or
JK) (where 99% of the data were used for learning and 1% for testing). In order to
determine the error in the machine learning system, the results from the testing phase
were compared with manual results, which were taken as the ground truth for risk
assessment. Because the manual results of the near wall risk assessment are likely to
have greater error than the machine learning system (due to the low intensity quality of
the ultrasound images from the near wall), the accuracy of the system will be principally
evaluated against the manual risk assessment in the far wall category.
The goal of this study is to propose a machine learning based sRAS. The main
innovations in this study are: (i) building a morphological-based risk assessment
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system using all three kinds of walls: far, near and combined far and near; (ii)
utilizing the LD stenosis as a ground truth for training the tissue characteristic based
system; (iii) embedding of automated recognition and segmentation of wall regions
with the risk assessment system; (iv) validating the risk assessment using manualtraced wall regions and computing the PoM; (v) optimization of the best kernel
during the classification paradigm; and (vi) understanding the size of the dataset
needed for developing a generalization versus memorization approach.
13.2 Demographics, data acquisition and data preparation
This section consists of the following subsections: section 13.2.1 discusses the patient
demographics; section 13.2.2 presents the data acquisition; section 13.2.3 presents
the ground truth data preparation; and the stratification of the ground truth into
high-risk and low-risk is presented in section 13.2.4.
13.2.1 Patient demographics
Two hundred and four (204) patients’ left and right CCA artery B-mode ultrasound
images were obtained from Toho University, Japan, and retrospectively analyzed
(with ethics approval from the institutional review board (IRB)). One patient had
only one image of the right CCA artery, therefore, the total number of images was
407. The patient demographics are shown in appendix B, table B1.
13.2.2 Data acquisition
In this study, a Japanese scanner (Aplio XV, Aplio XG, Xario, Toshiba, Inc.,
Tokyo) with a probe frequency of 7.5 MHz linear array transducer was used for
carotid scanning by our sonographer, who had 15 years’ experience. The acquisition
was adapted as per the recommendations and standards of the American Society of
Echocardiography Common Carotid Intima–Media Thickness Task Force. A full
ethics review by the IRB was approved along with written informed consent from
the patients. During the acquisition, the subjects were asked to lie in the supine
position with their head tilted backwards. Once the carotid arteries were detected in
the transverse view, the transducer was rotated 90° to acquire the anterior and
posterior views along the long axis view of the CCA (the blood flow direction). For
this database, the image resolution was 0.05 ± 0.01 mm. Figure 13.2 shows the
sample raw images of B-mode ultrasound.
13.2.3 Ground truth data preparation
For machine learning based stroke risk assessment, the training system requires the
ground truth information. We considered the LD as a gold standard for tracing of the
lumen–intima (LI) borders for the near (proximal) and the far (distal) walls in B-mode
carotid ultrasound scans. ImgTracer™ (courtesy of AtheroPoint™, Roseville, CA,
USA)—a commercial software package—was used for manual tracings of the LI
borders for the near/far walls of CCA. These delineated LI borders were then carefully
examined and endorsed by a neuroradiologist (author LS), with 15 years’ experience.
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Far wall
Near wall
Figure 13.2. Sample raw images of B-mode ultrasound corresponding to high risk (left) and low risk (right) on
the basis of the lumen diameter alone.
Near wall Near wall
MA
LI
LI
MA
Far wall Far wall
LI
LI
Far wall
Near wall
MA
MA
Figure 13.3. Ground truth tracing by an expert on high-risk (left) and low-risk (right) carotid scans.
A sample view of ImgTracer is shown in figure 13.3 for a high-risk (left) and lowrisk (right) patient. Each image has near and far walls showing the LI and media–
adventitia (MA) borders. The top dotted line for the wall indicates the LI interface
and the bottom dotted line indicates the MA interfaces, corresponding to the near
wall and far wall of the carotid artery. The region between the LI/MA walls is the
wall strip shown as the near wall strip and far wall strip, which were utilized for
grayscale feature extraction and risk stratification using the machine learning
system. The tracing protocol was adopted for all 204 patients, consisting of a total
of 407 carotid scans.
13.2.4 Stratification of manual LD into high risk and low risk
There are two reasons why we need the manual or ground truth LDs:
(i) to understand the distribution of manual LDs in our population. This will
help in designing the automated sRAS as a threshold criterion can be
developed for stratification.
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Figure 13.4. Stratification of ground truth population into high- and low-risk on the basis of LD.
(ii) To validate the sRAS, we need to design a manual risk assessment system
(mRAS). For this mRAS, we need the manual LDs.
Figure 13.4 shows the percentage distribution of our population into high-risk
and low-risk bins on the basis of the manual LDs changing from a maximum LD of
8 mm to a minimum LD of 5 mm in an interval of 0.2 mm, thus leading to 16 sets of
LDs. Red represents the high-risk patients and green represents the low-risk
patients. As the LD decreases from 8 mm to 5 mm with an interval of 0.2 mm,
the number of patients in the high-risk pool decreases, while the number in the lowrisk pool increases. Note that the distribution resembles a bell-shaped curve with the
highest population consisting of 6.2–6.4 mm. Appendix B, table B2 shows the
distribution of high-risk (HR) and low-risk (LR) patients in our population based on
LD threshold (LDT). Thus, depending upon the choice of LDT, one can stratify the
patients into high-risk and low-risk category.
13.3 Methodology
The fundamental concept in stroke risk stratification is to utilize the power of
grayscale texture features combined with the stenosis severity of the carotid artery.
Since plaque growth is multifocal in nature and never concentrated at one place, it is
therefore necessary to consider the hyper- and hypo-echoic distribution of grayscale
contrast all along the carotid arterial wall. Further, since the plaque growth has been
attributed to a complex disease consisting of internal factors, such as genetics, lipid
formation and blood pressure, and external factors, such as dietary conditions and
daily physical activity, there is no fixed plaque growth pattern and it leans toward
the class of randomness behavior [28]. Such randomness can be considered as
chaotic in nature which can be modeled in a fractal paradigm in computer models.
We thus model the grayscale wall contrast as a tissue characterization problem
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which, when combined with blockage severity, can be used for automated identification of high-risk and low-risk patients.
Note that the above grayscale features are computed in the wall region only. Since
the atherosclerotic plaque is present in the wall, we thus need an automated
segmentation protocol which can extract the IMT wall region for tissue characterization. Here onwards, we will interchangeably use the terms IMT wall region or
IMT wall strips, or simply ‘wall strips’. Thus our entire system consists of two major
steps: (a) automated wall segmentation for the near and far wall which has been
adapted from our recently published work [40] and (b) a risk assessment system for
stroke risk stratification based on tissue characterization in combination with
stenosis severity. The first subsection briefly discusses the technique adapted for
automated wall segmentation; the next subsection discusses the main blocks of the
sRAS, as shown in figure 13.5; finally, the last subsection presents the feature
extraction system.
13.3.1 Wall segmentation
The objective of the wall segmentation is to automatically delineate the LI and MA
borders for the near and far wall of the carotid artery. The overall system for wall
segmentation is composed of two stages: the global stage to extract the region-ofinterest (ROI) and MA borders for the near/far wall, and the local stage for
extraction of LI borders for the near and far wall.
During the global stage, we adopt a dependency approach where the goal is to
identify the adventitia region based on the physics of image reconstruction, which
hypothesizes that this region is brightest [45–50]. To detect these far adventitial
edges, a higher order derivative of a Gaussian filter is convolved with nearly the
same width as the carotid intima–media thickness (say, close to 16 pixels). Using this
as an origination point, we use a sweeping method along each column of the image
region and analyze the spectral signal to detect the peaks which corresponds to the
Figure 13.5. Global pipeline design for the sRAS and its validation.
Carotid B-mode Ultrasound
Automated Wall Segmentation
Wall Strips: Near & Far
Carotid Risk Assessment System
Stratification Results
Performance Evaluation
Precision of Merit
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MA of the near wall. Thus the MA of the near and far wall constitute the ROI of the
carotid region. The distance between the near/far MA walls constitute the IAD
(inter-adventitial distance). The local stage consists of LI extraction in the ROI
region. This is computed by first adapting the constant class model and extracting
the lumen region using a pixel-classifier approach. Here, we hypothesize that the
blood intensity in the lumen region is constant. In the post-binary region of the
lumen, one can obtain the edges of the LI for the near/far walls. LD is then estimated
by taking the mean distance between the near/far walls of the LI using the polyline
distance method [49]. Figure 13.6 shows the far wall strips of high-risk (left) and lowrisk (right) patients. Similarly figure 13.7 shows the near wall strips of the high-risk
(left) and low-risk (right) patients. The role of wall segmentation is shown in an
overall block diagram in figure 13.5.
13.3.2 Stroke risk assessment system (sRAS)
Figure 13.8 shows the machine learning system, the sRAS, that consists of offline
and online phases based on morphology-based tissue characterization. The offline
system comprises the training paradigm where the grayscale texture-based features
are computed, namely: (i) the gray-level co-occurrence matrix (GLCM), (ii) the
gray-level run length matrix (GLRLM) and (iii) chaotic features, totaling 16
features. The offline system uses the LD labels corresponding to high risk and low
risk, the morphology-based grayscale features and the support vector machine
(SVM)-training classifier [51–53] to generate the offline parameters. The online
system consists of risk prediction labels which consist of the transformation of online
a1
a2
a3
a4
Figure 13.6. Far wall strips of high-risk and low-risk patients. Left: high-risk patients shown in images (a1),
(a2), (a3) and (a4). Right: low-risk patients shown in images (b1), (b2), (b3) and (b4).
a1
a2
a3
a4
Figure 13.7. Near wall strips of high-risk and low-risk patients. Left: high-risk patients shown in images (a1),
(a2), (a3) and (a4). Right: low-risk patients shown in images (b1), (b2), (b3) and (b4).
b1
b2
b3
b4
b1
b2
b3
b4
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