nearly automatic liver vessels segmentation of computed ... · the blood vessels starting from a...
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Nearly Automatic Liver Vessels Segmentationof Computed Tomography Angiography
Patient Scans: Method and ExperimentalResults
A thesis submitted in fulfillmentof the requirements for the degree of
Master of Science
by
Miriam Natanzon
supervised byProf. Leo Joskowicz
The Selim and Rachel BeninSchool of Computer Science and Engineering
The Hebrew University of JerusalemJerusalem, Israel
August 16, 2009
Abstract
Advanced medical image processing is intensively studied to develop a highspeed and high accuracy imaging technology to be integrated throughoutthe clinical procedure starting by the diagnostic stage, onwards to preoper-ative planning and even real-time procedures. Automatic segmentation isconsidered a key task in developing any patient-specific application.This thesis presents a nearly automatic graph-based segmentation methodof the liver vascular system given a CTA image that can be integrated intovarious treatments of numerous liver diseases.
The method begins with liver contour extraction and partial segmentation ofthe blood vessels starting from a single user-defined pixel seed inside the liverarea. Then, the seed area is used for construction of a prior intensity proba-bility distribution function for liver blood vessels. The entire vascular systemof the liver is eventually segmented using a graph min-cut method based ona new edge weights function that adaptively couples the voxel intensity, theintensity prior, and geometric vesselness shape prior.Our method accurately segments the three main liver vessels with theirbranches: the Hepatic Artery, the Portal Vein and the Hepatic Vein. Itrequires only single seed point as user initialization and is completely free ofparameters adjustment. The suggested method is quite fast, it reaches goodcomputation-time on a standard PC (71-153 secs).
Examination of the method was done by two experiments, segmenting overall42 CTA images acquired from two sources taken by different machines. Eachexperiment was evaluated differently: expert radiologist’s evaluation provedour results to be high qualified (PV = 3.273 , MHV = 3.227 in a scale of1 to 4 ), comparison of the results to those generated by a semi-automaticsegmentation tool showed clearly that our method is significantly better thanexisting tools (added value for PV visualization is 3.5 for coloring and 1.75for spreading , in a scale from 1 to 4).
The results show that the method is highly satisfying medical requirementsfrom one hand, and is by far better than the offered implementations fromthe other hand. Other parameters including minimal user intervention re-quirement, good computation time and applicable memory usage simplifiesintegration of this method in various preoperative planning relevant in dis-eased liver treatment procedures.
Acknowledgments
I would like to express my gratefulness to many people that helped me, each
one in his way , along the process of writing this thesis.
First, I thank my supervisor, Prof. Leo Joskowicz, for his helpful and devoted
guiding during all the steps of my work from the implementation stage to
the design of the stucture of this thesis and for being always ready to give a
good advise.
I thank Moti Freiman, a Phd. candidate at Prof. Joskowitz lab that famil-
iarized me with the ITK software and gave great ideas of how to progress.
Also for his help in the evaluation stage.
Dr. Sosna from the Hadassah Medical center and the people from his labora-
tory provided us CTA images, performed accurate work of producing semi-
automatic segmentation and gave us essential information from a medical
point of view.
I thank Naama Lev-Cohen from Dr. Sosna group for her significant contri-
bution in the assessment of the results and for introducing the liver CTA
characteristics and common clinical procedures.
I thank my dear friend, Noah Broide for her friendship along all the way of
the M.Sc. studies and especially for the collaboration on working on this
project. It was all easier when you work with a closest friend..
I thank my parents that have directed me since my childhood to expand my
knowledge in all fields and gave me warmth and love. My whole family: my
grandparents, parents, sisters and brothers that helped me with taking care
of my children during the long hours I have worked in the laboratory.
My children, Yehonatan that got with understanding that ”Mom is in the
ii
university now”, and Adi that was inseparable part of the process of writing
even before she was born.
Last, but not the least, I would like to thank my husband, Yosef for his love
and understanding. Without your help and support during this period ,this
lines may not be written...
This research was supported in part by MAGNETON grant 38652 from the
Israeli Ministry of Trade and Industry.
iii
Contents
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Computed Tomography . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Clinical Background . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Method Overview . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Novel Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.6 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Literature Review 10
2.1 Intensity-Based Segmentation Approaches . . . . . . . . . . . 11
2.1.1 Thresholding Methods . . . . . . . . . . . . . . . . . . 11
2.1.2 Region Growing Methods . . . . . . . . . . . . . . . . 12
2.2 Geometric Shape-Based Segmentation Approaches . . . . . . . 13
2.2.1 Circular Object Detection . . . . . . . . . . . . . . . . 13
2.2.2 Matching Filters Methods . . . . . . . . . . . . . . . . 13
2.3 Active Contours . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4 Statistical Active Shape Models (ASM) . . . . . . . . . . . . . 14
2.5 The Graph-Cut Approach . . . . . . . . . . . . . . . . . . . . 15
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Method Overview 17
3.1 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2 Graph Structure . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 Graph Weights . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4 Min-Cut Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 23
3.5 Shape Criteria: Vesselness . . . . . . . . . . . . . . . . . . . . 23
iv
4 Experimental Results 27
4.1 First Experiment:
Hadassah Hospital Dataset . . . . . . . . . . . . . . . . . . . . 27
4.2 Second Experiment:
MICCAI 2008 Workshop Dataset . . . . . . . . . . . . . . . . 31
4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5 Conclusions 35
5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
v
List of Figures
1.1 Computed Tomography (CT) . . . . . . . . . . . . . . . . . . 2
1.2 Liver anatomy model. . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Liver dieases’s image-guided treatments . . . . . . . . . . . . . 6
3.1 two-mode algorithm results . . . . . . . . . . . . . . . . . . . 20
3.2 Seed area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 Graph-cut example . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4 Vesselness map . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.1 Artifacts examples . . . . . . . . . . . . . . . . . . . . . . . . 28
4.2 Comparison of Hadassah experiment results . . . . . . . . . . 30
4.3 Experiment final results . . . . . . . . . . . . . . . . . . . . . 34
vi
List of Tables
1.1 HU values range . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.1 Algorithm and workflow . . . . . . . . . . . . . . . . . . . . . 18
4.1 Hadassah experiment. . . . . . . . . . . . . . . . . . . . . . . 29
4.2 Miccai experiment. . . . . . . . . . . . . . . . . . . . . . . . . 32
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Chapter 1
Introduction
This thesis presents a nearly automatic graph-based segmentation method of
the liver vascular system given a CTA image, in purpose of patient-specific
medical procedures.
Section 1.1 explains the motivation for this work. Section 1.2 provides a
brief description of Computed Tomography (CT) and in particular Computed
Tomography Angiography (CTA). Section 1.3 provides some background for
the anatomy of the liver, possible diseases and their common image-guided
treatments. Section 1.4 presents and overviews the algorithm. Section 1.5
discusses the novel aspects of this thesis. Finally, Section 1.6 presents the
organization of this thesis.
1.1 Background
One of the great challenges of modern medicine is to find alternatives for fully
invasive surgeries in order to reduce the complexity and risks of such proce-
dures and minimize the recovery period as well. Medical image processing
among other computer-based technologies is a key component in achieving
this goal. By presenting an accurate picture of the intra-patient anatomy,
medical image processing has grown to be a useful tool in wide range of
clinical procedures including diagnostics, preoperative planning and image-
guided surgeries.
1
Chapter 1. Introduction 2
(a) (b)
Figure 1.1: CT scanning. (a) CT scanner. (b) CT slices.
Developing patient-specific techniques is a primary task in the world of
computer-aided surgery and medical image processing research, which leads
to intensive efforts in improving the accuracy, efficiency and automation
level of image-related hardware and software including facing with the inter-
patient high-variance anatomies, while preserving acceptable memory and
run-time performance in order to make them practical for physicians to use.
This thesis presents a nearly automatic method for blood vessels segmenta-
tion. The liver has a complex vascular system composed of three branched
vessels ended with thin capillaries: the Hepatic Artery, the Portal Vein and
the Hepatic Vein. Many medical treatments performed in response to various
liver diseases would be improved by trustworthy knowledge of location and
size of liver vessels from a precise segmentation. For example, ablation of tu-
mors and metastasis, infusion of chemotherapeutic dose and shunt (artificial
channel in the liver) insertion.
1.2 Computed Tomography
CT scanning was introduced by a British inventor Sir Godfrey Hounsfield
who was awarded a Nobel Prize for this great invention. This commonly used
2
Chapter 1. Introduction 3
Organ HU values rangeBone 1000Liver 40-60White Matter 46Grey Matter 43Blood 40Muscle 10-40Kidney 30Cerebrospinal Fluid 15Water 0Fat -50 - -100Air -1000
Table 1.1: Hounsfield Units (HU) values range for CT. Angio scans (CTA) pro-duce significantly higher HU values for blood 100-300 which highlights blood vesselsover other organs.
3D imaging technique was initially integrated in medical procedures only in
1974. From then until today, CT has grown to be an inseparable part of
various medical treatments including both preoperative and intra-operative
planning and diagnosis in general.
CT scanner (see figure 3.2(a)) is an advanced version of X-ray machine.
X-ray device consists of X-ray source and detector. The source sends out ra-
diation aimed to a specific area of the body that eventually hits the detector.
Different tissue types such as bones, muscles and blood, produce different
rate of radiodensity which is the relative transparency of a substance to X-
ray radiation. Radiodensity is measured using Hounsfield Units (HU), table
1.1 shows typical HU values for CT. The CT scanner involves, in addition,
a translating bed and both source and detector are positioned in opposite
sides on a ring-shaped structure that surrounds the patient. The patient is
lying on the bed that moves gradually through the ring while the ring is
revolving around, therefore, the radiodensity values are recorded for differ-
ent directions. This leads to more accurate and detailed information in each
horizontal slice joined together to 3D image.
Computed Tomography Angiography (CTA) image is obtained by a standard
CT scanner as described above with an additional preliminary step of con-
trast substance insertion in order to provide better identification of blood
vessels. CT images with contrast such as CTA can give a better distinction
3
Chapter 1. Introduction 4
and highlight specific organs. The contrast substance can be taken by mouth
or injected into an intravenous line.
Complex organs consisting of various tissue types are often required to follow
different CT scanning protocols in order to enhance visibility of the desired
element. The protocols differ in scan timing and infusion rate. Liver scanning
has several objectives. For each, radiologists follow the appropriate protocol
for example veins and arteries in the liver are acquired in different phases
known as ”hepatic phase”, ”portal phase” etc.
1.3 Clinical Background
The liver is a vital organ in the human body. It is located below the di-
aphragm, in the right upper quadrant of the abdominal cavity and its normal
weight for adults is around 1.5 kilogram. The liver has two different sources
for blood supply, the Hepatic Arteries and the Portal Veins which supplies 75
percents of the blood. The Hepatic Arteries supply the liver with oxygen-rich
blood from the hepatic system while the Portal Vein carries venous blood into
it. Unlike most veins, the Portal Vein does not carry blood directly to the
heart but through the liver. The liver processes the nutrient-rich blood which
comes from the gastrointestinal tract via the Portal Vein before it reaches
the systemic circulation and returns back to the heart .In addition, the liver
utilizes the metabolic pool contained in that blood to its functionalities. The
Hepatic veins function as regular veins, the de-oxygenated blood and the
blood supplied by the Portal Vein (to be cleaned in the liver), are merged
into those veins. Then, the merged blood is drained into the inferior vena
cava (IVC) which infuses it into the right atrium of the heart.
Both the arteries and veins split after getting into the liver to left and right
vessels that terminate in very small capillaries after several levels of bifurca-
tions. This creates separate branched trees for the liver blood vessels, as can
be seen in the model of liver illustrated in figure 1.2. The vessels branch-
ing separate the liver into several lobes. Some systems divide the liver into
four lobes while the Couinaud system [2] further divide into a total of eight
sub-segments.
4
Chapter 1. Introduction 5
Figure 1.2: Liver anatomy model. Image source: [1]
The liver plays a main role in various functions in the body including vital
processes of synthesis and breakdown. The liver converts glucose into glyco-
gen and store it, responsible for cholesterol and protein synthesis, produces
hormones and clean the blood from toxic substances. The liver functionality
is essential, it is involved in numerous biological procedures in the human
body and currently it is impossible to survive in the absence of it.
If the liver becomes inflamed or infected, its ability to perform these func-
tions may be impaired. Liver disease and infections are caused by a variety
of conditions including viral infections, bacterial invasion, and chemical or
physical changes within the body. The most common cause of liver damage
is malnutrition, especially that which occurs with alcoholism.
Cirrhosis and liver cancer are long- term effects of liver disease. Cirrhosis
is the term used to describe a diseased liver that has been severely scarred,
usually due to many years of continuous injury. Cirrhosis causes portal hy-
pertension, high blood pressure in the Portal Vein and its tributaries, which
frequently leads to intestinal bleeding or the buildup of fluid within the ab-
domen.
A TIPS (Transjugular intrahepatic portosystemic shunting) procedure is an
endovascular insertion and placement of an artificial channel (shunt) in the
5
Chapter 1. Introduction 6
(a) (b)
(c) (d)
Figure 1.3: Liver dieases’s image-guided treatments. (a)-(b) TIPS: Theshunt is used a bypass, connecting the Portal Vein directly to the Hepatic Vein.(c) RFA probe. (d) TACE. Illustration of the chemoembolization proccess. Imagesources: [3]-[6]
liver connecting the portal and the systemic venous system. A TIPS de-
creases the effective vascular resistance of the liver. The result is a reduced
pressure drop over the liver and a decreased portal venous pressure which
prevent from future bleeding to occur within high rate and might make less
fluid develop as well.
Modern liver cancer treatment allows non-surgical ablation of tumors and
metastasis known as Radio Frequency Ablation (RFA). In this procedure a
needle-like RFA probe is placed inside the tumor and the radiofrequency
waves passing through the probe increase the temperature within tumor tis-
sue that results in destruction of the tumor. In cases of unresectable tumors
6
Chapter 1. Introduction 7
or marginal liver functions, the only solution for complete healing is liver
transplant, but until such is performed (waiting period for liver donation is
usually long) the progression of the cancer must be delayed. Transcatheter
arterial chemoembolization (TACE ) is an interventional radiology catheteri-
zation procedure where a catheter is passed into the proper Hepatic Artery
and onwards to the branches of the Hepatic Artery supplying the tumor(s).
When a blood vessel supplying tumor has been selected, alternating aliquots
of the chemotherapy dose and of embolic particles, or particles containing
the chemotherapy agent, are injected through the catheter.
The method described in this thesis can significantly increase and improve
the use of non-surgical image-guided procedures as described above. Seg-
mentation of liver blood vessels can contribute to patient-specific modeling
for interventional radiology simulation.
1.4 Method Overview
In this thesis we present a nearly automatic algorithm for segmentation of
the liver blood vessels. The algorithm takes an abdominal CTA image as
input and outputs the segmentation result that can be used for accurate
diagnostics or for generating a patient-specific anatomy model.
We use the graph theory’s well-known ’Min-Cut Max-Flow’ theorem as the
main basis of our method. As a first step, the CT image is represented by a
weighted graph where its nodes identify voxels in the original image and its
edges, neighboring relations. The weighting function calculation is based on
both intensity and shape considerations. A seed area that is known to be a
part of the blood vessels is given to the ’graph-cut’ algorithm as an input and
used for initialization purposes by establishing a prior intensity probability
distribution function for the liver vascular system.
Since the graph-cut output is two disjoined groups representing the deviation
of the image to background and object, the relation of a voxel to the desired
segmentation object is defined accordingly.
We begin with finding the liver contour and a minor portion of the blood
7
Chapter 1. Introduction 8
vessels using Bayesian classification as the major method starting from a
given single user-defined point that can be marked even by not-professional
user [31]. This partial segmentation is functioned as initial seed area for the
’graph-cut’ performing.
Additional functionality we provide is what we call the ’two-mode’ option.
This allows the algorithm to choose the better parameters for the ’graph-cut’
stage considering the high-variance of input’s quality. The proper mode can
be determined before running the algorithm based on prior knowledge about
the image quality or as an accurate enhancing step, re-run the graph-cut
using the other mode.
1.5 Novel Aspects
Most existing software tools used for blood vessels segmentation require user
interaction in various levels (choosing seed points, tuning parameters dur-
ing initialization or throughout the process progressively), we offer a nearly
automated segmentation algorithm where user intervention level is negligi-
ble. The only requirement from the user is to supply a single point located
in the liver area, but even in cases of wrong initialization, the algorithm
would convergences into the right direction. This applies that our method is
easy-to-use even to non-expert user.
Our method deals successfully with the high variance of liver CTA images.
Low contrast is a typical situation in the input images that may be caused due
to pathological phenomena such as Cirrhosis or due to the scanning situation,
and makes the task of blood vessels detection very challenging. We suggest a
multiple-mode algorithm, allowing parameters adjustment according to the
image quality, done in reasonable computation time.
The method collects the information about the intensity values range from
the image itself and does not rely on earlier assumptions acquired from train-
ing set or based on medical literature. In result, we achieve stability and ro-
bustness i.e. dealing with the high-variance of image qualities differing from
one machine to another and applying this method to segmentation of blood
8
Chapter 1. Introduction 9
vessels in other organs.
In order to asses the proposed method we deliberately use two different eval-
uation techniques, the accuracy and contribution of the segmentation is ex-
amined in comparison to both the ideal and reality, medicine’s expectations
and existing industrial tools. The medical qualitative evaluation relies on
physicians expertise while the industrial comparison evaluation checks the
method’s contribution beyond other existing segmentation tools available in
the market. This two-scaled, wide range evaluation provides a better per-
spective of judgment.
The results of this thesis have submitted and accepted to the 2009 RSNA
conference [7].
1.6 Thesis Organization
This thesis consists of five chapters. Chapter 2 is a survey of previous work
in the field. Chapter 3 presents the proposed segmentation method. Chapter
4 describes our experiment, the evaluation technique and its results. Chap-
ter 5 concludes with a summary of our contributions and examines further
directions for future work.
9
Chapter 2
Literature Review
There are many automatic and semi-automatic vascular structure segmenta-
tion methods [8]. The main approaches rely on intensity values, geometric
shape, edge-based active contours and statistical active shape models. The
segmentation algorithms often yield inaccurate vessel diameters, miss ves-
sels segments and entire small vessels, and include non-vessel anatomical
structures and incorrectly model vessel bifurcations and pathologies. These
failures are often due to the local vessels appearance and characteristics vari-
ability in various regions of the body. In addition, most methods require
extensive user interaction and the adjustment of non-intuitive parameters,
which difficult their routine clinical use.
This chapter reviews previous works on vessels extraction. Section 2.1 and
Section 2.2 describe in detail the two main approaches: intensity-based and
geometric shape methods. These are the groundwork of our method de-
scribed in this work. Section 2.3 and 2.4 review other significant segmentation
methods. Section 2.5 presents previous applications usage of the graph-cut
method. Finally, section 2.6 summarizes the previous methods and applies
our new method’s benefit over them.
10
Chapter 2. Literature Review 11
2.1 Intensity-Based Segmentation Approaches
Intensity-based segmentation methods rely on the assumption that different
anatomies can be characterized by different Hounsfield unit (HU ) values.
There are many vessel segmentation methods of this kind; they utilize the
advantage of the CTA that sharpens the contrast between blood vessels and
other anatomies in the image.
2.1.1 Thresholding Methods
Thresholding is a simple but powerful technique. It yields segmentation over
an image by simply determining intensity values as threshold. The parti-
tion is done according to this threshold; all pixels within the same range are
grouped together. It does not take in consideration any spatial characteristic
and requires the choice of the threshold value. Its simplicity is a great ad-
vantage, which tempts many to use this, although its limitations which are
high sensitivity to noise, intensity in-homogeneities and to high variance of
areas of classes to be segmented. Therefore, it is usually not used as ’stand-
alone’ method but integrated with other methods. Most common usage of
this method is as pre or post processing such as noise removal and initial
filtering.
Higgins et al [9] and Niki et al [10] both perform a pure thresholding method
followed by connected component analysis. The main advantage of these
algorithms is their excellent run-time performance. Though, they are not
suitable enough to all anatomies, inaccurate results are expected when per-
formed on areas with high variance intensity values. Other works integrated
the basic thresholding method within the general process of their more com-
plex algorithms. Kim et al [11] use thresholding as a core action in its
algorithm. First, a histogram of the intensity values is used to initialize the
threshold value. Second, thresholding is performed locally on each slice of
the image, and determined adaptively. The Region of Interest (ROI ) in the
next slice is adjusted according to the segmentation result of the previous
slice. This algorithm utilize the benefits of thresholding as described above
(’fast and simple’), it overcomes thresholding’s main limitation by taking
11
Chapter 2. Literature Review 12
into consideration spatial proximity of pixels and avoids the need to choose
the threshold value by adaptive determination of this value, defined by a
histogram.
2.1.2 Region Growing Methods
Segmentation is performed based on two important criteria, value similarity
and spatial proximity. Two pixels can be grouped together if they have the
similar intensity characteristics or if they are physically close to each other.
It is assumed that pixels that are closed to each other and have similar
intensity values are likely to belong to the same object. The process starts
from a single or many seed points known to be part of the desired region, this
requires prior knowledge. The main disadvantage of region growing methods
in the context of vessel segmentation is that it can result in holes and over
segmentation due to the variations in image intensities (such as variations
caused by stents, made of metal or plastic, that are inserted into the vessel
in order to keep it open) and noise (such as nearby anatomical structures
with similar intensity range). Thus, it sometimes requires post-processing of
the segmentation result.
Revol-Muller et al [12] describe an advanced method of region-growing. They
performed an iterative process of region growing, increasing the threshold
value in each step. Eventually an assessment function is executed to de-
termine the most suitable result from the previous stage (region growing).
In their method they overcome some drawbacks of region growing. For in-
stance, not using a fixed threshold value helps avoiding the problems caused
by variations in image intensities, but this does require a longer computation
time.
12
Chapter 2. Literature Review 13
2.2 Geometric Shape-Based Segmentation Ap-
proaches
Geometric features of the desired anatomy drive shape-based segmentation
methods. Vessels segmentation algorithms will try to recognize tubular struc-
ture characteristics.
2.2.1 Circular Object Detection
Tubular structure can be identified by a sequence of circles in 2D planes. A
common method for this purpose is the ’Hough Transform’. The standard
Hough transform to locate round objects employs an edge detector. The
resulting edge information is used to find candidate center locations. Finally,
candidate center locations are averaged to obtain an estimate of the position
of the object’s center. In the 3D image result, the tubular structure will
be seen due to the sequential circles, found by Hough transform. The main
obstacle is that there might be nearby, non-vessel, tube-like anatomies and
the distinction between them is not an easy task. [13]
2.2.2 Matching Filters Methods
Matching filters convolves the image with multiple matched filters for the
extraction of objects of interest. In extracting vessel contours, designing
different filters to detect the vessels with different orientation and size plays
a crucial role. An orientation detection filter can use the eigenvalues of the
Hessian matrix i.e. second derivative eigenvalues to determine the likelihood
of a voxel to be a part of a vessel [14, 15]. Matching filters are usually followed
with some other image processing operations like thresholding and connected
component analysis to get the final vessel contours, to avoid segmentation of
any other tubular structured anatomies that may be extracted as well within
pure matching filter. In previous works [14, 15], a significant advantage is the
ability to cope with varying width of vessels, a common scenario e.g. in case
of segmentation of different vessels or different pathologies such as stenosis
13
Chapter 2. Literature Review 14
and aneurysms. But, this requires re-computation of the second derivative
values with multiple scales allow for different radius. There is a tradeoff
between accuracy and run-time performance; the exact extraction may be
problematic since in general the number of scales needs to be small to reduce
the computational cost.
2.3 Active Contours
Active contours (’Snakes’) is a model-based technique to find object contours
using parametric curves that deform under the influence of internal and ex-
ternal forces [16]. Physically, a snake is a set of control points in an image
that are connected to each other. Each one has an associated energy that
either rises or falls depending upon the forces that act on it. These forces are
known as snake’s internal and external forces, respectively. Internal forces
serve to impose smoothness constraints on the contour while external forces
pull the snake towards the desired image features like lines and edges. Vari-
ous vessels segmentation methods use the active contour technique and differ
in the energy function they define. Lorigo at al. [17] present an edge-based
active contour method. Its energy criterion is based both on intensity val-
ues and on local smoothness properties of the object boundary (vessel wall).
Nain at al. [18] combine image statistics and shape prior in their energy
function.
Active contour’s main disadvantage is that it usually requires user interac-
tion to initialize the snake. [19, 20] deal with this lack by automating the
initialization process. Additionally, Active contour is problematic in its ex-
cessive computing time and memory requirements. In general, active contour
is better for segmentation of local objects since snakes cannot move toward
objects that are too far away from the initial point.
2.4 Statistical Active Shape Models (ASM)
Statistical active shape models (ASM) [21] are statistical models of the shape
of objects which iteratively deform to fit to an instance of the object in
14
Chapter 2. Literature Review 15
a new image. The model is defined by a training set of segmented data,
its segmentation is represented by landmarks of the anatomy. The ASM
algorithm aims to match the model to a new image. The main drawback
is the training-set production, it requires involvement of an expert in the
field. Furthermore, strong noises in many medical images usually introduce
false edges that may trap the shape instance to incorrect locations. Some
techniques try to address this limitation. Lekadir et al [22] uses statistical
shape metrics based on inter-model landmark-based distances to revaluate
the error between the segmentation result of the instance and the model.
Another method that tries to avoid cases of wrong segmentation is the AAM
(Active Appearance Models) [23]. This method use the training set images
to estimate the relationship between model parameter displacements and the
residual errors induced between an image and an assignment in the model.
In order to segment a new image, AAM measurements are used to predict
changes to the current parameters that achieve better results. However,
AAM requires high-quality training images which can be very expensive,
while the former [22] does not consider the wealth of information provided
by the parameterized shape model.
2.5 The Graph-Cut Approach
A promising approach is the graph min-cut segmentation method [24, 25].
It classifies the voxel nodes that separate the objects of interest and the
background based on weighted voxel adjacencies. The advantages of graph
min-cut segmentation are that it is generic and that it is nearly parameter-
free. However, it relies on significant user interaction and requires fine-tuning
of the vessels intensity priors to capture the small vessels variability. It does
not incorporate vessels geometric information, is computationally intensive,
and has extensive memory requirements.
Recent improvements to the graph min-cut interactive segmentation method
address some of these drawbacks. Slabaugh and Unal [26] add an elliptical
shape prior term to the edges cost function that improves the segmentation
results. Though, in their work, this is done only on 2D images. Sinop and
15
Chapter 2. Literature Review 16
Grady [27] use a Laplacian pyramid to accelerate the segmentation and to
reduce the memory requirements. Ning et al. [28] improve the segmenta-
tion with graph-cut active contours based on prior object surface estimation.
Rother et al [29] reduce user interaction with user-defined enclosing rectan-
gular regions around the objects of interest. While this method is useful for
extracting simple objects in 2D images of natural scenes, it is laborious for
3D images of complex vascular structures. A common key drawback of these
methods is that their generic segmentation framework is often ill-suited for
volumetric vessels segmentation.
2.6 Summary
The reviewed methods offer different solutions to patient-specific segmen-
tation task. The challenge is to satisfy the many competing goals such as
accuracy, robustness, automation and efficiency. Each solution answers the
requirements in various levels and tries to overcome the contradiction be-
tween them.
The task presented in this thesis is a patient-specific application that can be
used as a pre-operative tool for treatment planning purposes. Therefore, it
should be fast and easy to use with the least user intervention as can be,
cope with high variability of input as in real-world data and most important,
produce an accurate and reliable result.
This thesis presents a graph-cut based method for vessel segmentation. The
core of the graph-cut process is the cost function. We define it as an inte-
grated function of both local intensity estimation and vesselness value (ves-
selness calculation is based on [15]). This overcomes weakness points of each
criterion when used as stand-alone. It is responsible for the method’s robust-
ness as well.
In contrast to previous works, our method finds the seed area almost au-
tomatically, therefore, it only requires negligible user interaction for initial-
ization. This trait makes it practical and compatible with patient specific
applications.
16
Chapter 3
Method Overview
This chapter describes an automatic graph based segmentation method of the
liver vascular system. The method uses min-cut algorithm to vary between
the object (liver blood vessels) and the background. It utilizes the prior
characterization of blood vessels in CTA image, i.e. intensity range and
shape.
The contents of this chapter are as follows. Section 3.1 describes the al-
gorithm in general. Section 3.2 describes the graph structure. Section 3.3
explains how the graph weights are determined. Section 3.4 provides an
overview of the min-cut algorithm. Finally, Section 3.5 describes into details
the shape criteria calculation.
3.1 Algorithm
Given an abdominal CT image with injected contrast liquid (CTA), we pro-
pose a nearly automatic segmentation algorithm to extract the liver’s blood
vessels. The main idea of this algorithm is to distinguish between the object
voxels and the background voxels, where the object is the desired segmenta-
tion area. To do so, we use the graph-cut algorithm.
Initially, we need to build the graph which represents the input image. The
nodes in the graph represent voxels in the image, in addition there are two
17
Chapter 3. Method Overview 18
Input: a CTA image of the abdominal area.Output: Binary image which segments the vascular structure in the liver.
Main method: ’graph-cut’
1. Build a graph that represents the image.
(a) Each pixel is a node in the graph.
(b) Two dummy nodes - object and background.
(c) Edges from each pixel to it’s six immediate neighbors.
(d) Edges from each pixel to the dummy nodes.
2. Calculate edge weights. Based on intensity and vesselness values.
3. Perform ’min-cut’ algorithm.
Workflow:
1. Extract the Region of Interest - The liver surface.
2. Identify partial area of blood vessels.
3. Create the vesselness map for the Region of Interest (ROI ).
4. Run ’graph-cut’ algorithm on the Region of Interest, use the preliminaryvessel segmentation result (from step 2) as seed.
5. Noise removal - choose the largest connected components.
Table 3.1: Segmentation of liver blood vessels: Algorithm and workflow -schematic view.
18
Chapter 3. Method Overview 19
dummy nodes representing the object and background accordingly. Edges
represent connections in the image. Every two neighbor voxels in the image
have a connecting edge between them. This applies all three axes; therefore,
each node will have six neighbors. Two additional edges are drawn from each
node in the graph: one to the object node and the other to the background
node. These edges represent the probability of a voxel to belong to the
object or background correspondingly. Weights are assigned to the edges as
a combined function of intensity values and shape characterization. Now, we
perform the graph-cut algorithm which outputs the desired partition of the
image nodes to object nodes vs. background nodes.
Liver CTA images often differ in their quality. The reasons are various,
including pathological cases such as sick patient (high level of cholesterol)
and different quality of CT machines. To overcome this common obstacle
we offer a two-mode procedure of the graph-cut algorithm using different
radius values for the vesselness map calculation. In general, this allows a
more precise segmentation as can be seen from figure 3.1.
As a preliminary step, we find simultaneously the liver contour and a small
portion of the liver vessels in the CTA (see figure 3.2 for examples). For this
purpose we use a nearly automatic algorithm for liver analysis, described
in details in [31]. This method was examined on various datasets [32] and
was graded within best segmentation results. Method seemed to be robust
and accurate and as consequence is well-suited for the purpose of finding
initial good seed area. The process is based on multi-resolution iterative
approach. It requires single user-defined pixel seed inside the liver that can be
marked even by non-expert user since the method is robust enough and copes
with not-accurate initial point. The algorithm starts with generation of an
intensity model describes the distributions of the Hounsfield units values for
liver and other organs in the abdominal CTA dataset. Based on this model,
each voxel is classified as Liver parenchyma, tumor, vessel, or background
using the Bayesian classification method. Adaptive morphological operators
are applied to identify the liver, fill holes inside the liver and disconnect
it from surrounding organs. Finally, active contours method is applied to
refine the liver surface. Then, the algorithm repeats those steps- generate
19
Chapter 3. Method Overview 20
(a)
(b) (c)
Figure 3.1: Illustration of the two-mode algorithm. (a) The grayscaleinput. In this case the segmentation task is very challenging due to the poorquality of the CTA image. (b) The first mode result. Segmentation is noisy. (c)The second mode result. Segmentation is more accurate.
again the intensity model and the histogram and so on, according to the
knowledge acquired from the process. The process continues to iterate until
convergence, when no changes to the liver surface are detected.
The vesselness map is generated for the Region of Interest - the liver area
extracted in the previous stage to retrieve the shape information per voxel.
Voxels within the seed area found in the first step are assuredly object vox-
els. We use this preliminary knowledge within the graph-cut algorithm in
order to characterize the object’s intensity features (i.e. mean and standard
deviation).
20
Chapter 3. Method Overview 21
(a) (b)
Figure 3.2: Seed area. Partial segmentation of the blood vessels serves as aseed area to the ’graph-cut’ algorithm
3.2 Graph Structure
We use the graph-cut method to distinguish between the ’object’ (vascular
tree) and the ’background’. All image voxels are nodes in the graph. The
graph contains two additional dummy nodes representing the ’object’ and
the ’background’. An edge is ”drawn” between each voxel to each of its six
immediate neighbors: two in the x axis, two in the y axis and two in the z
axis and to each of the dummy nodes: one to the ’object’ node, and another
one to the ’background’ node.
Formally, let graph G = (V,E) where V = {vi, s, t} are the voxels nodes (vi)
and object (s) and background (t) nodes, and E = {(vi, vj), (vi, s), (vi, t)} for
each voxel vi and its 6-neighbor voxels (vj).
3.3 Graph Weights
Edge weights are divided into two kinds in accordance to the edge types:
(a) The edge weight between a node to its neighbor is an intensity based
normalized ’distance’ function. In this manner, high distance value will re-
21
Chapter 3. Method Overview 22
sult with low weight value and vice versa. (b) The vascular structure can
be characterized by two attributes: typical intensity range of blood vessels
(˜100–300 HU) and tubular structure (vesselness, based on Frangi’s formula
[15]).
Since the role of an edge weight between a node to a dummy node is to
evaluate the relevance of a voxel to the object (vascular tree) or background,
the weight calculation is a combined function of both voxel’s intensity and
vesselness.
We use the mean and standard deviation values computed from the seed area
which is known to be an object.
Let:
µobj = mean value of the object intensity values
σobj = standard deviation of the object intensity values
Formally, for each (vi, vj) ∈ E where vi and vj are adjacent, the weight
function W is:
W (vi, vj) = exp
(−(
(I(vi)− I(vj))
σ
)2)
(3.1)
where σ calculation is based on σobj. (Tuned according to the voxel vesselness
value, as far as the vesselness value is high , we let a wider variance.)
For each (vi, s) ∈ E, the weight function W is:
W (vi, s) = exp
(−(
(I(vi)− µobj)
σobj
)2)M(vi) (3.2)
where M(vi) is the vesselness value.
For each (vi, t) ∈ E, the weight function W is the complement of the above
function:
W (vi, t) = exp
(−(
(I(vi)− µobj)
σobj
)2)
(1−M(vi)) (3.3)
22
Chapter 3. Method Overview 23
3.4 Min-Cut Algorithm
Cut C is defined as a subset of edges such that the removal of C from the
graph G separates the source from the target nodes, in our case, the two
dummy nodes s and t. A cut defines two disjoint subsets, the object set O
and the background set B, such that s ∈ O and t ∈ B. The cost of a cut is
defined as the sum of weights of the cut edges e ∈ C:
|C| =∑e∈C
We (3.4)
The optimal cut is the minimum-cost cut. It can be efficiently computed
using standard graph theory algorithms. The theorem of Ford and Fulkerson
[30] states that the minimum-cost cut can equivalently be calculated with the
Max-flow algorithm. The idea behind Ford and Fulkerson algorithm is very
simple. As long as there is a path from the source (s ∈ O in our graph) to the
sink (t ∈ B), with available capacity on all edges in the path, we send flow
along one of these paths, which means we decrease the edge weights along
the path that produces the residual graph. Then we find another path, and
so on. A path with available capacity is called an augmenting path. When
no more augmenting paths can be found, s will not be able to reach t in the
residual graph. If S is the set of nodes reachable by s in the residual graph,
then the total capacity of edges from S to the remainder of all the nodes in
the (original) graph (V − S) is on the one hand equal to the total flow we
found from s to t, and on the other hand serves as an upper bound for all
such flows. This explains why the flow we have found is maximal.
3.5 Shape Criteria: Vesselness
One of the characteristics of a vascular structure is its tubular shape. We use
a Hessian-based measure in order to determine voxel’s relevancy to a vessel
like structure at a given scale. The measure describes the geometric structure
of vessels by analyzing the eigensystem of the 3×3 Hessian matrix H of each
voxel from the volumetric image. For bright vessels on a dark background,
23
Chapter 3. Method Overview 24
Figure 3.3: Graph-cut example. Illustration of a 2D image graph and its cut.
the following shell exists:
λ1 ≈ 0 (3.5)
λ1 � λ2
λ2 ≈ λ3
The eigenvector associated with λ1 is the direction along the vessel, therefore
we expect its eigenvalue to tend to zero. The eigenvectors associated with λ2
and λ3 are perpendicular to λ1. Therefore, we expect λ2 and λ3 to be greater
then λ1, with a low difference.
24
Chapter 3. Method Overview 25
Figure 3.4: Vesselness map. Shape-based probability map of each voxel to belongto the object according to Frangi’s vesselness measure.
We use Frangi’s vesselness measure [15]:
(3.6)
M(σ) =
0 λ2 > 0 or
λ3 > 0,1−exp
− R2A
(2a)2
exp
− R2B
(2b)2
1−exp
− S2
(2c)2
otherwise
where:
RA =|λ2||λ3|
, RB =|λ1|√|λ2λ3|
, S =√λ2
1 + λ22 + λ2
3
and σ is the scale at which the measure is computed. RA differentiates be-
tween plate and line-like structures, RB measures the deviation from blob-like
structures, and S differentiates between foreground (vessel) and background
(noise). Constants a, b and c are predefined weights determining the influ-
ence of RA, RB and S. The vesselness measure value is close to 1 for voxels
with tube-like structures, and close to 0 otherwise. The vesselness measure
is computed for each voxel and for several scales. The maximal value among
of the different scales is chosen as the vesselness of the current voxel. This
25
Chapter 3. Method Overview 26
measure behaves like a probability map which maps the probability of each
voxel to be a vessel. Figure 3.4 illustrates the vesselness response overlaid on
the original datasets. The colors are for visualization purpose only.
26
Chapter 4
Experimental Results
We implemented our segmentation method using a powerful open-source
toolkit known as ITK (Insight Toolkit) [33] which implements state-of-the-
art algorithms in medical image processing and analysis. To visualize the
CTA images and their segmentation results we used ITK-Snap [34], a very
user-friendly software for presenting 3D images. We have tested our seg-
mentation method on a series of 42 abdominal CTA images taken from two
different datasets. The first dataset was taken from the radiology depart-
ment at Hadassah hospital from Dr. Sosna’s lab. The second dataset was
provided by the MICCAI 2008 3D Liver tumors segmentation workshop [32].
The results evaluation was done differently for each dataset.
The contents of this chapter are as follows. Section 4.1 describes the exper-
iment and the results of the Hadassah CTA images. Section 4.2 describes
the experiment and the results of the MICCAI CTA images. Section 4.3
discusses and summarizes the experimental results.
4.1 First Experiment:
Hadassah Hospital Dataset
The dataset contains twenty scans taken from ten patients in age range of
32 to 79 years and average age of 54 years, consist of both hepatic phase
27
Chapter 4. Experimental Results 28
(a)
(b) (c)
Figure 4.1: CTA images with artifacts. (a) Cholesterol accumulation yieldspoor contrast. Whiter pixels can be seen on the top right edge. (b) A stent isdetected as high intensity area. (c) Tumors and metastasis are detected as blackholes.
and portal phase images for each patient. The scans were acquired on a 64-
channel detector scanner (Brilliance 16, Philips Medical Systems, Groningen,
The Netherlands) with 2 mm slice thickness,1 mm increment, 200-250 mAs
and 120 kVp. These CTA images had 512 × 512 × ˜400 (The Z axis vary
between the images, the average size is around 400) voxels. The clinical
findings of these scans included healthy livers and livers with various diseases
such as cirrhosis, metastases and primary liver tumors. Artifacts in the CTA
image can result from unhealthy livers such as these i.e. tumors, stents
and fatty liver, or from circumstantiated factors revolving around the scan’s
timing. See figure 4.1 for examples.
28
Chapter 4. Experimental Results 29
Portal Vein Hepatic Veinmain vessel coloringquality spreading main vessel coloring
quality spreading artifacts
3.5 1.75 2.25 2.25 0.75
Table 4.1: Hadassah experiment. Average results of segmentation comparisongrading. The first two columns present scores of both coloring and spreadingmeasures for the Portal Vein (PV). The third and fourth columns are the samefor the Hepatic Vein (HV). The fifth column presents artifact rate for the entireimage.
We compared the results of our method to those obtained from an extrac-
tion of the liver vessels performed by a dedicated technologist using a semi
automatic paintbrush tool in a Philips EBW3.0 workstation, (Philips Medi-
cal Systems, Groningen, The Netherlands), which serves as a representative
example of the segmentation abilities currently present. This comparison
was done by an expert radiologist. The evaluation consists of the following
measures:
Coloring quality: on a scale from 0 to 4, where 0 indicates similar coloring
quality between the representative example and the segmentation result
and 4 indicates significant improvement of the segmentation over the
representative example. Coloring quality measure takes into account
both accuracy and consecutiveness.
Spreading level: on a scale from 0 to 4, where 0 indicates similar spreading
level between the representative example and the segmentation result
and 4 indicates significant improvement of the segmentation over the
representative example. Spreading level indicates till which peripheral
location the segmentation has reached.
Artifacts rate: on a scale from 0 to 4, where 0 indicates the segmenta-
tion has no additional artifacts over the representative example and
4 indicates the high rate presence of artifacts in the segmented image
compared to the representative example.
Table 4.1 summarizes the evaluation of our results. In order to give a more
specific and useful evaluation, the first two measures, coloring quality and
29
Chapter 4. Experimental Results 30
(a)
(b) (c)
Figure 4.2: Comparison of Hadassah experiment results. (a) The input -2D slice grayscale image. (b) and (c) are the semi-automatic tool and our methodoutputs correspondingly. Note the high coloring quality and spreading level in(c).However, the segmentation in (c) suffered from the presence of artifacts.
spreading level referred to the Hepatic and Portal Vein separately. Artifacts
rate assessment referred to the entire segmented region. Since our automatic
segmentation generated more detailed results, the lowest grading was equiva-
lent to similarity of the two, any higher scoring proves our results to be better
and more accurate. Note that we accomplished significant improvement in
both coloring quality and spreading (e.g. coloring quality average for PV is
3.5 and for HV is 2.25) while the artifacts rate is negligible (average of 0.75).
30
Chapter 4. Experimental Results 31
4.2 Second Experiment:
MICCAI 2008 Workshop Dataset
The dataset contains twenty two portal phase scans gathered for the purpose
of liver tumor detection competition organized by MICCAI [32]. As such,
they are representative sample of real world cases. The scans were acquired
on one 64-channel detector and two 40-channel detector CT scanners using
a standard four-phase contrast enhanced imaging protocol with 1-1.5 mm
slice thickness and in-plane resolution of 0.6-0.9mm. These CTA images had
512 × 512 × ˜350 (The Z axis vary between the images, the average size is
around 350) voxels.
The evaluation method used in this experiment is the more common one.
An expert radiologist determines the grading according to his experience in
the field. The radiologist assessed the visibility of individual vessels in the
3D segmentation images in comparison to the original CT images using the
following measures: zero indicates poor visualization and four indicates excel-
lent visualization. Eight main liver venous vessels were examined including
Portal Vein branches as well as the three Hepatic Veins.
Table 4.2 summarizes the results for those cases and the overall results. Note
that our method successfully segmented the LPV and LHV (average grading:
3 and 3.045 correspondingly) despite their relative low contrast in the original
CT images.
4.3 Summary
The two experiments described above differ in both their dataset source
(Hadassah hospital, MICCAI workshop) and the used evaluation method.
Each evaluation method examines the contribution of our algorithm in a dif-
ferent aspect. In the first experiment, the evaluation procedure was based on
a comparison between our segmentation results to such generated by an up-
to-date industrial tool. From this experiment we conduct that our method
gives more accurate and detailed results which is presumably a great advan-
31
Chapter 4. Experimental Results 32
RHV MHV LHV PV RPV LPV RAPV RPPVMICC-01 4 4 4 4 4 4 4 4MICC-02 1 1 1 1 1 1 1 1MICC-03 4 4 4 4 4 3 4 4MICC-04 1 1 1 4 4 4 4 1MICC-05 2 2 2 4 4 4 4 4MICC-06 1 1 1 1 1 1 1 1MICC-07 4 4 4 4 4 4 4 4MICC-08 4 4 4 4 4 4 4 4MICC-09 3 3 3 4 4 4 2 2MICC-10 4 4 4 4 4 1 4 4MICC-11 4 4 4 4 4 3 4 4MICC-12 4 4 4 4 3 4 2 2MICC-13 4 2 1 4 4 4 4 4MICC-14 4 3 1 1 4 1 2 2MICC-15 4 4 4 4 4 4 4 4MICC-16 4 4 4 4 4 3 4 4MICC-17 3 3 2 3 3 3 3 3MICC-18 4 4 4 4 4 4 4 4MICC-19 1 3 2 1 3 2 2 2MICC-20 4 4 4 4 4 4 4 4MICC-21 4 4 4 1 1 1 1 1MICC-22 4 4 4 4 3 4 4 4Average 3.273 3.227 3 3.273 3.409 3.045 3.182 3.045
Table 4.2: Miccai experiment. Evaluation grading for 22 CTA scans. Dividedinto the eight main liver venous vessels: (1) RHV - Right Hepatic Vein (2) MHV- Middle Hepatic Vein (3) LHV - Left Hepatic Vein (4) PV - Portal Vein (5) RPV- Right Portal Vein (6) LPV - Left Portal Vein (7) RAPV - Right Anterior PortalVein (8) RPPV - Right Posterior Portal Vein. The scoring range is 0 to 4.
tage over the existing tools presented up until now. In the second experiment,
the evaluation was done by a physician grading our results according to his
knowledge and experience concerning medicine’s requirements and needs.
The high scores we achieved in this experiment indicate that our method
fulfils the medical expectations above and beyond.
The sufficient results described in Table 4.1 and Table 4.2, were generated
with good performance. The mean computation time on a standard PC
dual-core 2.4GHz with 3GB of memory machine was 94.15 secs (std=33.1
secs).
The experiment’s input contains various types of datasets which covers high
32
Chapter 4. Experimental Results 33
range of different phenomena, as can be met in real-world clinical proce-
dures. In all cases, we have accomplished high level results in reasonable
computation time and acceptable memory usage.
This qualifies our method to be easily integrated in a preoperative planning as
it meets the physician’s requirements: nearly automatic, fast enough response
time and applicable with standard resources.
33
Chapter 4. Experimental Results 34
(a) (b)
(c) (d)
(e) (f)
(g) (h)
Figure 4.3: Experiment final results. In the left column, the 2D results aredisplayed. The right column presents the corresponding 3D segmentation.
34
Chapter 5
Conclusions
This chapter concludes the thesis. Section 5.1 summarizes the background,
goals, and contributions of this thesis. Section 5.2 suggests directions for
improvements and future work.
5.1 Summary
This thesis presents a nearly automatic method for liver blood vessels segmen-
tation from CTA images that can highly improve the preoperative planning
process in clinical treatment for various liver diseases.
Many previous works suggests wide variety of vascular structure segmenta-
tion techniques. The existing methods don’t offer a sufficient solution for the
purpose of automatic or nearly automatic segmentation of the liver blood
vessels that can be merged into a clinical routine. Some of the methods
don’t cope successfully with the significant inter-patient liver geometry and
intensity variances or the artifacts of many kinds. Other methods are not
applicable to clinical situation because they require extensive user interven-
tion in selecting many seed points or tuning parameters or due to long run
time and expensive resources.
The segmentation method suggested in this thesis is based on the ’graph-
cut’ algorithm which is known to be cheap in the term of run time. It
35
Chapter 5. Conclusions 36
takes into account both intensity and shape considerations and looks for
vessels with different radius. This yields that our method produces impressive
segmentation results of the entire liver vascular system including all three
main vessels, the Hepatic Artery, the Hepatic Vein and the Portal Vein, and
their branches, all done in a reasonable time.
We examined the method on overall 42 ”real-world” CTA images acquired
from two sources, using different machines with different qualities. The im-
ages create a representative model reflecting the variance in liver CTA images:
different anatomies, presence of pathologies and artifacts.
We used two different evaluation methods in order to examine the contri-
bution of our method in two different aspects: the ideal objective from a
medical point of view and the added value to existing tools in the industry.
We achieved high quality results that answer the strict requirements of ac-
curacy and reliability as medicine states. In addition, our method proved
to be significantly better in comparison to a widely used segmentation tool
assumed to be one of the best in the field.
In conclusion, we present an accurate, robust, easy-to-use segmentation method
that can be integrated into image guided procedures such as liver tumors
treatments (e.g. chemoembolization, radiofrequency).
5.2 Future Work
Since automatic blood vessels segmentation is relevant in various patient-
specific applications for different anatomies, the demand for such is con-
stantly increasing by the medical community. These segmentations can be
used as a preliminary step for producing an input to training simulators
or as an accurate estimate of situation report detailing critical information
such as size and location of blood vessels, required before taking an invasive
procedure e.g. in brain surgeries.
Therefore, the main direction for possible future work is to apply this segmen-
tation method on blood vessels in other anatomies besides the liver’s vascular
system. First attempt in this direction has already been taken; our method
36
Chapter 5. Conclusions 37
segments successfully the carotid arteries and its full vascular tree [35]. As
we expected, we can use our core method with few minor adjustments for
other blood vessels segmentations, this is possible thanks to the robustness
and generalization of our method and its nearly complete independence of
parameters. Any implementation of our method for other organs would re-
quire some adjustments and modifications i.e. anatomy-related features like
seed area and range of radius sizes must be customized accordingly. Further-
more, it is foreseen that CTA images of separate anatomies will challenge our
method with their own unique features, different common pathologies and
artifacts. Other vessels that we plan to segment with this method are the
Abdominal Aorta for detection of Aneurysm (known as Triple A or AAA)
and brain vessels.
Another direction for future work is to achieve accurate deviation of the liver
according to Couinaud ’s segment model [2] based on this liver blood vessels
segmentation.
We hope that this method will assist the influential goal of improvement of
the clinical pre-operation step and increase treatment’s accuracy in surgeries
from many kinds.
37
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