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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_3592_Библиотеки_им_академика_М_И_Перельмана
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Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
It was found that the time-averaged WSS is more reduced in aneurysms far away
from the curvature peak. This larger reduction is attributed to the secondary flow
being reduced further downstream from the curvature peak. The haemodynamic
variable reduction can be correlated to vascular morphology near the aneurysm.
This was further studied in a follow-up clinical study [52].
5.5.5 Flow diverter length change and future research
A novel method for the computation of changes in flow diverter length has been
proposed recently [53]. This method rapidly computes the length of a braided device
when released inside a vessel. This method is designed to aid the interventional
neuroradiologist during treatment. The aim is to provide, in real time, a prediction
of the change in length of the FD when being placed in the patient’s anatomy. The
challenge comes with the fact that currently, FDs are braided devices. Because of
this, a change in diameter of the device implies a substantial change in its total
length. Furthermore, the irregular and tortuous geometry of cerebral vasculature
make it very complicated to predict the final length of an FD when placed in the
patient anatomy.
The method, initially assessed in [54], is capable of estimating the proximal end
point of the device based on quantitative information of the patient anatomy and the
distal release position of the stent (figure 5.9(a)). This method has been tested in FDs
placed in real patient anatomies. In figure 5.9 (b) are shown two views for a Silk stent
(Balt Extrusion, Montmorency, France) placed in an internal carotid artery. It can
be observed that the total length of the device is accurately estimated, although the
Figure 5.9. (a) Schematic representation of the anatomical descriptors of the vascular district. The function
relates the local morphology of the vessel with the length change of the FD. This results in a different
fr()
change in the total length of the device according to the position where it is deployed inside the vessel.
5-16
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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vascular anatomy is complex and tortuous. This simulation indicates that a total
change of 62% more than the total FD length was observed after placement in the
patient anatomy. Its robustness and sensitivity to segmentation of the vessel
geometry was also assessed, showing a good performance and tolerance to error
in the segmentation threshold [55].
The performance of this method has also been clinically evaluated when used to
simulate different brands and types of braided stents, showing an accuracy of over
92% in average, when assessing the final length of the implanted device [56, 57].
Simulation of flow diverter porosity is yet another tool with a promising future in
the selection of devices for aneurysm treatment. This technique is still under
assessment, and further results will evidence its clinical potential [58].
Furthermore, its combination with CFD has the potential of allowing its use inside
the clinic, due to its computationally lower cost [59–61].
The predictive value of computational models is greatly appreciated in the clinic.
The ability to plan one or more treatment alternatives and being able to assess their
outcome can help in identifying potentially harmful or dangerous situations. Also,
the fact that such tools can be used within the intervention room or, equivalently,
obtain a response in real time, opens the possibility of them being used in day-to-day
clinical practice.
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Section III
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Vessel and stent segmentation
https://t.me/medicina_free
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 6
Graph-based cross-sectional intravascular image
segmentation
Ehab Essa, Xianghua Xie, Huaizhong Zhang, James Cotton and Dave Smith
We present a fully automatic segmentation approach to detect the media–adventitia
border in intravascular ultrasound (IVUS) and the lumen border in optical
tomography (OCT) images. A graph-based segmentation method is developed to
accurately estimate the borders. Segmentation in IVUS and OCT has been shown to
be an intricate process due to the relatively low contrast and various forms of
interferences and artifacts caused by, for example, calcification, stents and acoustic
shadows. Graph-cut-based methods often require careful manual initialisation and
produce inconsistent tracing of the border. We propose unravelling the image and
transferring the object segmentation into a height field segmentation in polar
coordinates. Thus, the border of interest is obtained by searching a minimum closed
set on a node weighted directed graph. We use a double-interface automatic graph
cut technique to prevent the extraction of media–adventitia border in IVUS from
being distracted by those image features. The cost functions are derived by using a
combination of complementary texture features. For OCT, a novel image feature is
incorporated into the solution scheme, which is derived from a vector field that takes
into account gradient vector interactions across the image domain. In addition,
Laplacian diffusion is employed to improve the performance of our method for
dealing with excessive noise. Evaluation results demonstrate that our method
achieves better performance compared to a number of alternative segmentation
techniques.
6.1 Introduction
IVUS and OCT imaging are catheter-based technologies, which show twodimensional cross-sectional images of the coronary structure. There are two types
of borders of interest: the lumen–intima border, which corresponds to the inner
doi:10.1088/2053-2563/ab01fach6 6-1 ª IOP Publishing Ltd 2019
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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arterial wall, and the media–adventitia border, which represents the outer coronary
arterial wall. The appearance of both borders in IVUS or OCT images is affected by
various forms of imaging artifacts, such as acoustic shadows caused by the catheter
guide-wire, calcium in IVUS, or the stent in OCT.
Among many other techniques, formulating the IVUS and OCT segmentation as
a combinatorial optimisation [6, 7, 10, 14, 18, 20, 21, 24, 27] of a cost function based
on local image features has been a popular approach. In [18], dynamic programming
is used to search a minimum path based on a cost function that incorporates edge
information with a simplistic prior relying on assumed echo pattern and border
thickness. Manual initialisation is generally necessary. In [21], the border detection is
carried out on the envelope data before scan conversion. The authors applied spatiotemporal filters to highlight the lumen, based on the assumption that the blood
speckles have higher spatial and temporal variations than the arterial wall, followed
by a graph-searching method similar to [18]. However, image features introduced by
acoustic shadows or a metallic stent would seriously undermine their assumption.
Catheter movement can also cause spatial and temporal fluctuations, which lead to
ambiguities. The s–t cut method [14] is employed in [24] to segment 3D IVUS data.
The vertical intensity pattern along the borders, the Rayleigh distribution and the
Chan–Vese minimum variance criterion are used in designing the cost functions.
These intensity-based features are susceptible to image variations that commonly
exist in IVUS, such as calcification and acoustic shadows.
Several methods rely on user interaction to obtain a good result [1, 2, 10, 20].
However, these methods can be time-consuming and impractical with a large image
size and/or a large number of images. For example, in the conventional graph cut
[1, 2], user interaction is necessary to infer the unary cost for each pixel. In addition,
the definition of smoothness pairwise cost is mainly derived from edge features,
which becomes less useful in the obscure regions of the image. In [20], a semiautomatic graph-based method is proposed that repetitively requires the user to
interactively correct the segmentation result on the longitudinal view until satisfactory segmentation is achieved.
Li et al [14] proposed a terrain-like multiple surface segmentation method by
constructing a weighted directed graph that allows imposing some geometrical
constraints to define the elasticity of each surface and the inter-relation with other
surfaces, and to search for the minimum closed, subgraph set containing the surface
on its envelope by utilising the s–t cut method to minimise the cost function and
without the need for user intervention. This method is well suited to IVUS/OCT
segmentation, however, dealing with image artifacts, defining the optimal cost
function and adapting the graph construction are the major challenges, as described
in [ 6, 7, 27].
In this chapter, a bottom-up data-driven approach is presented to segment IVUS
and OCT images. For IVUS segmentation, a double-interface graph cut segmentation is proposed to delineate the media–adventitia border, in order to achieve
reliable results automatically. The impediments, such as stents or fibrotic and
calcium plaques, appear inside the media–adventitia border, and the acoustic signal
decays rapidly in the adventitia, so there are generally no strong features beyond the
6-2
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media–adventitia border. This observation inspired us to apply an additional
interface searching inside the media–adventitia border which links those undesired
image features, including partial lumen border, and hence preserves the border of
interest. A combination of complementary texture features is used to form the basis
of the boundary-based cost functions. For OCT segmentation, a single-interface
graph cut segmentation is proposed to delineate the lumen border. Moreover, a
novel image feature is incorporated into the cost function, instead of merely using
image intensity or the local gradient magnitude. The image feature is derived from
the gradient vector interaction across the image domain and possesses the characteristics of regional features.
6.2 Pre-processing
The pre-processing step is to transform images from Cartesian coordinates to polar
coordinates and to remove the catheter region from the transformed images.
Representing the images in polar coordinates is desirable to facilitate feature
extraction with equal emphasis on radial and tangential dimensions. It also
facilitates the automated graph cut in searching for minimum closed sets.
Moreover, the post-processing can then be carried out more efficiently since it
becomes a one-dimensional interpolation instead of two-dimensional.
The catheter generates a blank region which contains no information and is
surrounded by a ring-down artifact which may hamper the search process for finding
the minimum cost path for the desired border. The ring-down artifact is located in
the first rows of the transformed image, and it is approximately a constant.
Therefore, a simple thresholding method is used to remove that region, as shown
in fi gure 6.1(c).
6.3 Feature extraction
In IVUS imaging, the media layer largely consists of homogeneous smooth muscle,
which exhibits as a dark layer in ultrasound images, and the adventitia layer tends to
be brighter, see figure 6.1 as an example. Hence, edge-based features are appropriate
to extract the media–adventitia border. In OCT imaging, the lumen appears to be
much darker because blood is flushed out before imaging. The intima and other
tissues, including plaque, surrounding the lumen have a bright appearance. Hence,
the lumen–intima border shows good contrast, i.e. image gradient features may be
Figure 6.1. (a) An original IVUS image. (b) Polar transformed image. (c) After removing the catheter region
(green curve shows the ground-truth of the media–adventitia border).
6-3
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adopted to highlight the border. However, a guide-wire artifact and other interfering
image features commonly exist inside the artery and they cast shadows over the
border of interest, disrupting its continuity. Those imaging artifacts generally have
large responses to image-gradient-based feature extraction.
6.3.1 Steerable filter
A steerable filter is a linear combination of differently oriented instances of the base
filter. A set of n order derivatives of Gaussian (GD) filters
Gxy(, )
n
in different
orientations can be used to highlight the edge features along the border. The
steerable filters can be defined as a linear combination of a set of Gaussian
derivatives [9]:
where
Gxy(, )
θ ⩽⩽
j
θθ
Gxy k G xy(, ) () (, ),
n
θ
n
jM(),1
is the rotated version of
are interpolation functions.
∑
=
jM1
j
θ=
j
n
Gxy(, )
n
at θ orientation and
(6.1)
Steering derivatives in the direction of the gradient makes them invariant to
rotation. These steerable filters are more effective in highlighting oriented structure,
e.g. edges, than isotropic band-pass filters, particularly when there is noise
interference [9].
6.3.2 The log-Gabor filter
The Gabor filter acts as a band-pass filter that has been used in texture analysis to
exploit its similarity with the human visual system [5, 22] and performs multichannel, frequency and orientation analysis on the visual image. The Gabor filter
can achieve optimal joint localisation in the spatial and frequency domains. Gabor
filters have two components, a real part and an imaginary part, where the Gabor
function is a multiplication of a Gaussian function and a complex sinusoid function
in the spatial domain, corresponding to a Gaussian shift from the centre of
frequency in the Fourier domain. Here, the log-Gabor filter [8] is used in different
scales to enhance the border and to reduce speckles and other image artifacts. The
log-Gabor function,
, has a frequency response defined as a symmetric
Gf()
Gaussian on a log frequency axis:
() exp
=−LG f
⎛
[log( )]
⎜
2[log( )]
⎝
ff
σ
2
⎞
0
, (6.2)
⎟
2
f
⎠
0
where f0is the centre frequency of the filter, and σ is the filter bandwidth. The logGabor function has no DC component for any bandwidth filter compared to the
Gabor function.
6-4
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