MEDICAL IMAGE SEGMENTATION SYSTEM FOR CEREBRAL ANEURYSMS by Yuka Sen ...

222 Pages·2015·13.04 MB·English

MEDICAL IMAGE SEGMENTATION SYSTEM
FOR CEREBRAL ANEURYSMS
by
Yuka Sen
Dissertation submitted in fulfilment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Department of The Australian School of Advanced Medicine
Faculty of Medicine and Health Sciences
Macquarie University
Sydney, Australia
February 2015

ABSTRACT
Background: Ruptured intracranial aneurysms is a much studied topic, with
reports indicating the presence of unruptured intracranial aneurysms in approx-
imately 5% of the adult population. Although the rupture rate of intracranial
aneurysms is not high, it may lead to serious consequences including disabil-
ity and mortality. Current treatments for intracranial aneurysms, however, also
carrysignificantrisks. Tocounterthis, accurateassessmentofthepotentialforin-
tracranial aneurysm rupture is thereby essential in order for clinicians to balance
the risk of surgery against the risk of the natural history.
In current medical practice, greater emphasis is placed upon medical imaging
technologies, including CTA, MRA and DSA scan for diagnostic purposes. These
are widely applied in neurovascular imaging as a non-invasive diagnostic tool for
the detection and evaluation of intracranial aneurysms. This makes it possible
to visualize three dimensional (3D) cerebral aneurysms, the results allowing us
to be able to reconstruct patient-specific vessels and aneurysms. Currently, the
3D geometry blood vessel has been applied in the performance of haemodynamic
simulations, with the results obtained subsequently applied as a tool for the di-
agnosis of aneurysm risk and in support of neurosurgeons for the treatment of
aneurysms. Visualization and haemodynamic simulations are all based upon the
results of medical image reconstruction - aimed at the extraction geometries of
targetedintracranialaneurysmsfromthree-dimensional(3D)medicalimages. De-
spite the many image segmentation methods available, with varying approaches
and algorithms, no dominant method yet exists, in terms of effectiveness, across
the cerebral aneurysm. It has been indicated that it is necessary to develop a
method in order to accurately segment the cerebrovascular aneurysm; thereby
allowing us to measure aneurysm volume, size, and its 3D shape.
Methods: In this thesis, I proposed a new method of segmentation called the
Threshold-based Level Set (TLS) method. This method was specifically designed
for application in cerebrovascular and cerebral aneurysms, and was based upon
the Geodesic Active Contours model and Chan-Vese model (CV), integrating
both region and boundary information to segment cerebral aneurysms through
the use of a global threshold and gradient magnitude to form the speed function.
Validation tests have been carried out to ensure the quality of the proposed TLS
methodinboth3DCTAscanand3DDSAscanimages. Bothin-vivoandin-vitro
validation tests were performed. In the in-vivo experiment, forty five aneurysm
patients, including vascular and cerebral aneurysm CTA imagery across three
locations; the internal carotid artery (ICA), middle cerebral artery (MCA) and
anterior communicating artery (AComA), were used for the validation of the TLS
method via analysis of the forty five TLS segmented models in terms of geometric
shape, volume, and haemodynamic results. In the in-vitro experiment, however,
a series of CT scans of silicone aneurysm models were conducted in this study,
with four different silicone models and four rates of contrast agent dilution used
to generate various image data sets for validation of the TLS method.
Results: The proposed TLS method was found to be able to accurately seg-
ment intracranial aneurysms with blurred boundaries, complex cerebrovascular
anatomical shapes and inhomogeneous images under automatic conditions. By
comparison and contrast to other approaches, the TLS method revealed the high-
est volume overlap rate (JM), and lowest volume difference (VD), with its most
important advantage featuring its ability to identify the complex local geometry
of intracranial aneurysms - extremely important information in clinical applica-
tion. The results from in-vivo validation showed that the TLS method appears
higher in terms of overlap ratio and smaller in terms of volume difference than
the other methods for ICA, MCA, and AComA aneurysms. The study likewise
indicated that the volume differences and the overlap ratio of TLS may be con-
trolled at a maximum of under 9% and a minimum of over 92% for all aneurysm
locations respectively. In-vitro validation results showed that the TLS method
was able to achieve over 89% of the volume overlap rate and under 7% of the
volume difference across all different degrees of silicone model shape complexities
and contrast agent dilutions.
Conclusion: The TLS method is a technique with the ability to automatically
segment intracranial aneurysms without the setting of a seed point or intensity
threshold, and is likewise available for the segmentation of modifiable anatomical
shapes, with blurred boundaries and inhomogeneous images. The TLS method
may thus be a useful tool in the assistance of clinical diagnosis and surgical
preparation, and play a vital part in the future of computational haemodynamics
research.

STATEMENT OF CANDIDATE
I certify that the work in this thesis has not previously been submitted for a
degree nor has it been submitted as part of the requirements for a degree to any
other university or institution other than Macquarie University.
I also certify that the thesis is an original piece of research and it has been written
by me.
In addition, I certify that all information sources and literature used are indicated
in the thesis.
..................
Name Of Candidate

ACKNOWLEDGMENTS
As a mother of two, completing a PhD while juggling family and academic
responsibilities have been quite an arduous task and would not have been ac-
complished without the invaluable support and supervision that my supervisors,
colleagues and family afforded me. I am forever indebted to your contributions.
I firstly would like to thank Professor Michael Morgan, his tireless supervision
and his expansive expertise and clinical knowledge a real asset to my work, and
I wish him well for any of his future endeavours.
I am also extremely grateful for Professor Alberto Avolio, whose support and
continuous interest in my project through the provision of my scholarship being
of tremendous benefit to me.
Further, I extend my thanks to Professor John Magnussen, whose expertise in
medical imaging and CT scanning as well as his input in my experiment assisting
me greatly. I know he will be a tremendous asset in any field that he pursues and
I wish him well for the future.
Also, a big thank you to Sylvia Priilaid who was also fundamental through-
out this project and assisted me with all of my administration paperwork and
other documentation for my study, while also being a wonderful friend to have a
conversation with.
I also want to thank Dr Wei Sheng Zhang for his assistance in my CT scan,
as well as Dr. David Verrelli for his contribution in that experiment, as well as
teaching me much about statistical analysis.
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Furthermore, I would like to thank both Dr. Jing Long Liu, who taught me
how to work the CFD software fundamental to my work, and Dr. Chang-Joon
Lee, who never falteringly answered my CFD questions.
I am also grateful to the entire team at Macquarie Medical Imaging (MMI)
who have been a wonderful asset and have helped me procure much of the data
necessary for my study.
This thanks is also extended IT group at Macquarie University, especially
Hans Brabandt, IT support officer for ASAM, without their help in procuring a
Matlab license, much of this study would have not been possible.
I also thank all the members both past and present of the Macquarie Univer-
sity CFD group for their support throughout this process.
Lastly, I extend my thanks to my family and friends, especially my husband,
Itsu, and my children, Rida and Kenta, who continued their unwavering support
and patience all throughout the process, even when I wasn(cid:48)t in the best of moods.
Thank you for putting up with me, and thank you for soldiering on with the
housework and other menial chores when I wasn(cid:48)t able to help.