Stroke Lesion Segmentation for tDCS

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Stroke Lesion Segmentation for tDCS

Elin Naeslund

LiTH-IMT/MI30-A-EX--11/502--SE

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Stroke Lesion Segmentation for tDCS

Examensarbete utfört i medicinsk informatik

vid Tekniska högskolan i Linköping

av

Elin Naeslund

LiTH-IMT/MI30-A-EX--11/502--SE

Handledare: Mats Andersson

IMT, Linköpings universitet Lucas C Parra

BME, The City College of New York

Examinator: Hans Knutsson

IMT, Linköpings universitet

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Avdelning, Institution

Division, Department Medical Informatics

Department of Biomedical Engineering Linköpings universitet

SE-581 85 Linköping, Sweden

Datum Date 2011-06-07 Språk Language Svenska/Swedish Engelska/English Rapporttyp Report category Licentiatavhandling Examensarbete C-uppsats D-uppsats Övrig rapport

URL för elektronisk version

xx xx ISBN — ISRN LiTH-IMT/MI30-A-EX--11/502--SE

Serietitel och serienummer

Title of series, numbering

ISSN

—

Titel

Title

Segmentering av strokelesion för tDCS Stroke Lesion Segmentation for tDCS

Författare

Author

Elin Naeslund

Sammanfattning

Abstract

Transcranial direct current stimulation (tDCS), together with speech therapy, is known to relieve the symptoms of aphasia. Knowledge about amount of current to apply and stimulation location is needed to ensure the best result possible. Segmented tissues are used in a finite element method (FEM) simulation and by creating a mesh, information to guide the stimulation is gained. Thus, correct segmentation is crucial. Manual segmentation is known to produce the most accu-rate result, although it is not useful in the clinical setting since it currently takes weeks to manually segment one image volume. Automatic segmentation is faster, although both acute stroke lesions and nectrotic stroke lesions are known to cause problems.

Three automatic segmentation routines are evaluated using default settings and two sets of tissue probability maps (TPMs). Two sets of stroke patients are used; one set with acute stroke lesions (which can only be seen as a change in image inten-sity) and one set with necrotic stroke lesions (which are cleared out and filled with cerebrospinal fluid (CSF)). The original segmentation routine in SPM8 does not produce correct segmentation result having problems with lesion and paralesional areas. Mohamed Seghier’s ALI, an automatic segmentation routine developed to handle lesions as an own tissue class, does not produce satisfactory result. The new segmentation routine in SPM8 produces the best results, especially if Chris Rorden’s (professor at The Georgia Institute of Technology) improved TPMs are used. Unfortunately, the layer of CSF is not continuous. The segmentation result can still be used in a FEM simulation, although the result from the simulatation will not be ideal.

Neither of the automatic segmentation routines evaluated produce an accept-able result (see Figure 5.7) for stroke patients. Necrotic stroke lesions does not affect the segmentation result as much as the acute dito, especially if there is only a small amount of scar tissue present at the lesion site. The new segmentation rou-tine in SPM8 has the brightest future, although changes need to be made to ensure anatomically correct segmentation results. Post-processing algorithms, relying on morphological prior constraints, can improve the segmentation result further.

Nyckelord

Keywords automatic segmentation, manual segmentation, stroke lesions, tDCS, SPM8, apha-sia patients

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Abstract

Transcranial direct current stimulation (tDCS), together with speech therapy, is known to relieve the symptoms of aphasia. Knowledge about amount of current to apply and stimulation location is needed to ensure the best result possible. Segmented tissues are used in a finite element method (FEM) simulation and by creating a mesh, information to guide the stimulation is gained. Thus, correct segmentation is crucial. Manual segmentation is known to produce the most accu-rate result, although it is not useful in the clinical setting since it currently takes weeks to manually segment one image volume. Automatic segmentation is faster, although both acute stroke lesions and nectrotic stroke lesions are known to cause problems.

Three automatic segmentation routines are evaluated using default settings and two sets of tissue probability maps (TPMs). Two sets of stroke patients are used; one set with acute stroke lesions (which can only be seen as a change in image inten-sity) and one set with necrotic stroke lesions (which are cleared out and filled with cerebrospinal fluid (CSF)). The original segmentation routine in SPM8 does not produce correct segmentation result having problems with lesion and paralesional areas. Mohamed Seghier’s ALI, an automatic segmentation routine developed to handle lesions as an own tissue class, does not produce satisfactory result. The new segmentation routine in SPM8 produces the best results, especially if Chris Rorden’s (professor at The Georgia Institute of Technology) improved TPMs are used. Unfortunately, the layer of CSF is not continuous. The segmentation result can still be used in a FEM simulation, although the result from the simulatation will not be ideal.

Neither of the automatic segmentation routines evaluated produce an accept-able result (see Figure 5.7) for stroke patients. Necrotic stroke lesions does not affect the segmentation result as much as the acute dito, especially if there is only a small amount of scar tissue present at the lesion site. The new segmentation rou-tine in SPM8 has the brightest future, although changes need to be made to ensure anatomically correct segmentation results. Post-processing algorithms, relying on morphological prior constraints, can improve the segmentation result further.

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Acknowledgments

I would like to thank my examiner Hans Knutson for his help and input during this thesis work, for letting me follow my dream and sometimes pulling me back to earth. I would also like to thank my supervisors, Mats Andersson at IMT and Lucas C Parra at The City College of New York, for answering my questions and pointing me in the right direction when needed.

I would also like to thank the people in the Neural Engineering group at the BME department at The City College of New York; Abhi and Davide for answering my sometimes silly questions about Matlab and LaTeX, Asif for the discussions we have had, and finally thank you Johnson, Belen, and Christoph for all the laughs we shared. Thank you for making my months at The City College of New York the best possible. I will miss every single one of you.

Finally, I would like to thank my family for always believing in me. Thank you for all your love and support over the years, in school and in life in general. A thanks also goes out to my friends, for making the years at Linköping University the best possible. I love you.

Elin Naeslund New York, May 2011

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Acronyms

AC anterior commissure

CNS central nervous system

CSF cerebrospinal fluid

DCS direct current stimulation

DWI diffusion weighted imaging

FEM finite element method

fMRI functional MRI

FWHM full width half maximum

GM gray matter

LOM lesion overlap map

MOG mixture of Gaussians

MNI Montreal Neurological Institute

MRI magnetic resonance imaging

PC posterior commissure

ROI region of interest

SPM statistical parametric mapping

tDCS transcranial DCS

TMS transcranial magnetic stimulation

TPM tissue probability map

WM white matter

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Chapter 1

Introduction

Stroke patients are sometimes in need of therapy, e.g. speech therapy to regain the ability to speak again after a stroke affecting the speech center located in the left hemisphere. This in combination with therapy involving changing the plasticity of the brain with for example current or a magnetic field has shown positive results. To ensure an efficient therapy, the physician has to have knowledge about on what locations on the head to apply current and how strongly to do so. Thus, segmentation of the brain to gain knowledge about the anatomy is needed since the tissues present have different electrical conductivity. For example, a necrotic stroke lesion filled with cerebrospinal fluid (CSF) has a higher conductivity of the current within the brain. An acute lesion may also influence the electrical conductivity since there is some change to the brain tissue. Thus, accurate segmentation is the key.

To break down the main purpose of this thesis work, a problem formulation was made based upon the project proposal written by professor Lucas C Parra at The City College of New York. Related research within the field, along with improvements already proposed and made, is also specified. Finally, the purpose of the thesis work is summarized into a number of goals and the organization of the report is explained for easier reading.

1.1 Problem Formulation

Segmentation of stroke lesions is of great importance during treatment of patients with for example aphasia, which is a disease connected to the ability to formulate words. One way of treating these patients is by applying direct current, a method named direct current stimulation (DCS), to the patient’s brain. Recent stud-ies have shown that transcranial DCS (tDCS), a method of applying the current through a number of scalp electrodes, in combination with speech therapy, relieves the symptoms of aphasia for a period of time. Another method, named transcra-nial magnetic stimulation (TMS), has been used within this field and involves a depolarization in the neurons of the brain using a magnetic field [1]. Patients find tDCS less uncomfortable with only a slight tingling at the electrodes, while TMS

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6 Introduction

is known to cause discomfort and pain.

To know how much current to apply, and on what area of the skull to do so, knowledge about the anatomy of the brain and how electrical signals are conducted within it is crucial. Applying current is easier on patients with a ’normal’ brain anatomy since the anatomy of the brain is usually known. If the patient for example has suffered a stroke, the anatomy of the brain changes, thus changing the way electrical signals are conducted. This is why there is a need for correct segmentation of brains with abnormal anatomy.

Today there is a choice of performing the segmentation manually, which is very time consuming but produce a more reliable result, or to use one of the automatic segmentation routines available, some of which give a poor segmentation result but is faster than the manual dito. Thus, the ideal would be a more robust automatic segmentation routine which can handle the changes in brain anatomy that appear after a stroke.

This thesis will start with a thorough literature study involving for example the anatomy of the brain, possible changes in the brain after a stroke, and the theory that the automatic segmentation routines are based upon. An investigation of a current automatic segmentation routine named SPM8 and the improvements made on it will also be made.

1.2 Related Research

The software that will be used during this thesis is SPM8, developed by the Well-come Trust Centre for Neuroimaging, London, UK. During the past few years, improvements have been made on the original segmentation routine in SPM8. Among these improvements are Chris Rorden’s, professor at The Georgia Institute of Technology (www.sph.sc.edu/comd/rorden/), new and improved tissue prob-ability maps (TPMs), Mohamed Seghier’s, doctor at the Wellcome Trust Centre for Neuroimaging, lesion identification routine, and the new segmentation routine in SPM8 (only work in progress). These improvements along with the original segmentation routine are explained in Section 3.

1.3 Purpose and Goals

The purpose of this thesis is to evaluate a number of automatic segmentation routines in presence of both acute stroke lesions and necrotic stroke lesions. It can be summarized into a number of goals.

• Perform a study of the previous work performed within this field.

• Analyze how the original segmentation routine in SPM8 works in presence of both acute stroke lesions and necrotic stroke lesions.

• Analyze how the new segmentation routine in SPM8 works in presence of both acute stroke lesions and necrotic stroke lesions.

• Perform and analyze the segmentation result from the new segmentation routine in SPM8 when using Chris Rorden’s improved TPMs.

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1.4 Organization of the Thesis Report 7

• Analyze and compare the segmentation results from the segmentation rou-tines in SPM8 with Mohamed Seghier’s segmentation routine (ALI) devel-oped to handle stroke lesions.

• Compare the results from the automatic segmentation routines with available manual segmentation.

• Make conclusions on which automatic segmentation routine performs the best for stroke patients.

1.4 Organization of the Thesis Report

The chapters of the thesis report include the following information.

Chapter 2 includes an overview of the human brain anatomy, some information about what happens in the brain after a stroke, and how this affects the segmen-tation result. An introduction to brain stimulation is also included together with a brief description of manual segmentation and automatic segmentation.

Chapter 3 describes previous work performed in the field of segmentation of human brains, with and without stroke lesions present. The original segmentation routine in SPM8 is described together with the new segmentation routine in SPM8. The improvements made by Chris Rorden and Mohamed Seghier are also explained along with information about the manual segmentation performed by the Neural Engineering group at The City College of New York. The different settings are explained together with possible problems that may arise.

Chapter 4 gives a description on how the segmentation routines will be com-pared to each other, along with some information on how the results will be pre-sented and evaluated. The patients used in the evaluation are also prepre-sented.

Chapter 5 summarizes the results from the evaluation of the original segmenta-tion routine in SPM8 and the improvements made on it. The manual segmentasegmenta-tion performed by the Neural Engineering group at The City College of New York is also included as the correct way of segmenting the brain when applicable.

Chapter 6 contains a discussion and interpretation of the results, conclusions that can be made, and ideas on possible development and improvements in the future.

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Chapter 2

Theory

The human brain has a complex structure and is dependant upon many things, e.g. nutrition and oxygen delievered by the blood. If the blood flow to the brain is changed in some way, there are consequences leading to a change in brain anatomy and brain physiology. A stroke, if left untreated, will lead to a (stroke) lesion. A lesion is defined as a change in brain anatomy and thus, also a change in electrical conductivity of the tissue. Due to this, there is a need to ensure proper segmen-tation. This will be described in this chapter along with a brief introduction to tDCS since this thesis work is part of ongoing research related to that subject. A brief introduction to manual segmentation and automatic segmentation is also included.

2.1 The Human Brain

This section includes a brief introduction to the brain, based upon information found in [2]. The brain, together with the spinal cord, makes up the central nervous system (CNS). The brain can be divided into the telencephalon, the cerebellum, the diencephalon, and the brainstem. An illustration of the parts of the brain can be seen in Figure 2.1. The telencephalon is made up of two areas; the cerebral cortex (in pink) and the basal ganglia (not marked). The cerebral cortex is the largest part of the brain, with a surface area of about 2200 cm2_{. This}

part of the brain is for example involved in the process of thinking, learning, and remembering.

The cerebral cortex is built up by neurons and unmyelinated fibers giving it a gray color, thus this part of the brain is commonly referred to as the gray matter (GM). Below the GM is a large mass of axons that connects the cerebral cortex with other regions of the brain. Since the axons are myelinated, the tissue has a white color, hence this region is often referred to as the white matter (WM). The basal ganglia has indirect connections with the cerebral cortex, thus this part of the brain is involved in motor control.

The cerebellum is located posterior and inferior of the telencephalon and con-tains about 50% of the neurons found in the CNS. There is a large number of

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10 Theory

input connections to this part of the brain, e.g. visual input and auditory in-put. The diencephalon can be divided into the thalamus, the subthalamus, and the hypothalamus. These areas are involved in remembering, releasing hormones, and controlling body temperature and hunger. The brainstem is located directly posterior of the spinal cord and receives sensory information and sends out motor signals through cranial nerves.

Figure 2.1: Illustration of brain regions [3].

Surrounding the brain and the spinal cord is CSF, which is a clear bodily fluid. It acts as a ’cusion’, protecting the brain inside the skull both mechanically and immunologically. CSF is produced at a number of locations within the brain and after circulating the brain, it is absorbed by the venous blood. Around 500 ml of CSF is produced every day and since the brain can only contain around 150 ml, the rest is drained into the blood stream. Thus, the CSF turns over nearly four times daily.

A longitudinal fissure divides the brain into two hemisheres, connected through the corpus collosum. The hemispheres resemble each other and the structure of each hemisphere is generally mirrored by the other. Despite their similarities, the hemispheres have different functions, although both hemispheres are involved in every task performed by the brain. The left hemisphere is often called the ’domi-nant’ hemisphere and includes the language center. The left hemisphere processes information in a logical and sequencial manner. The right hemisphere, involved in interpreting visual information and spatial processing, processes information intuitively and randomly [4]. In general, the right part of the brain controls the left part of the body and vice versa.

The brain is divided into a number of lobes, which can be seen in Figure 2.2, each one responsible for different tasks [5]. The frontal lobe is involved in higher functions, e.g. interpreting touch, vision, and hearing. The motor cortex is located in the posterior part of the frontal lobe and is involved in body movements. The temporal lobe, located inferior of the motor cortex, is involved in understanding language, the memory process, and hearing. The parietal lobe, located posterior to the frontal lobe, is involved in interpretation of language and sensation of touch,

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2.1 The Human Brain 11

pain, and temperature. The occipital lobe, located in the posterior part of the brain, is where visual input is interpreted.

Figure 2.2: Illustration of brain lobes [6].

2.1.1 A Healthy Brain

The anatomy of a normal, healthy brain might look like the brain seen in Figure 2.3. In an image from a magnetic resonance imaging (MRI) investigation, different tissues show up with different image intensities [7]. In a T1 weighted image, seen in Figure 2.3a, WM shows up the brightest with a white color, followed by GM represented by a light gray color. CSF has the lowest image intensity and shows up black. In a T2 weighted MRI, seen in Figure 2.3b, CSF instead shows up bright white and WM shows up in a darker image intensity. GM is represented by a gray color, similar to that in a T1 weighted MRI. More information about MRI can be found in [7].

2.1.2 Strokes

A stroke is defined as ’an interruption of the blood supply to any part of the brain’ [8]. This occurs when a blood vessel transporting blood, and oxygen, to the brain is blocked by a blood clot (ischemic stroke) or when a blood vessel is damaged, causing blood to leak out into the brain (hemmorhagic stroke). Since the brain is very sensitive to loss of blood supply, the patient may suffer major damages following a stroke. The cells in the surrounding tissue either die due to lack of oxygen or are compressed due to the leakage of blood, thus the increased pressure later killing them. The symptoms of a stroke depend on which part of the brain is affected, e.g. weakness or paralysis of an arm (motor cortex affected), vision changes (visual cortex affected), and difficulty speaking or understanding spoken words (language center affected). Although some stroke patients will not have any symptoms at all.

There is a need to start treatment within three hours from the onset of a stroke to ensure the best chance of recovery. Immediate treatment is for example drugs

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12 Theory

(a) T1 weighted MRI. (b) T2 weighted MRI.

Figure 2.3: Axial slices illustrating the difference in image weighting of MRIs. Images kindly provided by Julius Fridriksson, associate professor at The University of South Carolina.

that break up blood clots if the patient has suffered an ischemic stroke. If the patient instead has suffered a hemmorhagic stroke, surgery is often required to remove the pool of blood in the brain and repair the damaged blood vessel. Long term treatment involves recovering lost body functions, e.g. physical therapy to learn how to walk again, and preventing future strokes. About 10% of all stroke patients regain all functionality and about 50% of all stroke patients are able to leave the hospital with some medical assistance.

2.1.3 Stroke Lesions

When a patient suffers a stroke, the brain tissue changes due to the lack of oxygen supply or the increased pressure. The stroke lesion is initially classified as an acute lesion, the difference can be seen only as a slight change in image intensity in an MRI. An example of this can be seen in Figure 2.4a, where the lesion is defined as the white oval shaped part in the right hemisphere.

After some time, the tissue becomes necrotic and is cleared out by scavenger cells leaving just a hole in the tissue. The void is later filled with CSF. When this has happened, the lesion is classified as necrotic or chronic. An example of a necrotic lesion can be seen in Figure 2.4b. A part of the left hemisphere is affected, i.e. the darker area together with the enlarged left ventricle. The enlarged left

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2.2 Stimulation of the Brain 13

(a) Acute stroke lesion. (b) Necrotic stroke lesion.

Figure 2.4: T1 weighted axial slices with different stroke lesion types (acute and necrotic). Images kindly provided by Julius Fridriksson, associate professor at The University of South Carolina.

ventricle is due to a shift in the brain following the clearing of tissue at the lesion site. Instead of normal brain matter there is a bit of nothing, a ’hole’ in the brain, filled with CSF. There is also some scar tissue, with image intensity similar to that of GM, at the lesion site. Due to the location of the lesion, this patient suffers from aphasia, a disorder connected to the ability to speak and understand language. The type of aphasia depends on which part of the language center is damaged, e.g. Broca’s area is connected to speech control and Wernicke’s area is connected to speech interpretation.

This kind of damage is irreversible, although there are techniques used today where some of the symptoms can be reduced by for example applying current at numerous locations on the skull of the patient [9]. One method of interest for this thesis work is tDCS, more information about this can be found in Section 2.2.

2.2 Stimulation of the Brain

Non-invasive brain stimulation has shown positive results when used on for exam-ple aphasia patients and is a promising technique according to Rossini et al [10]. tDCS involves passing low intensity direct current into the patient’s head via a set of scalp electrodes [11]. Usually, one anode (stimulation) electrode and one cathode (return) electrode are used to supply and pick up the current applied. The skull shunts some of the current due to the high resistivity between the skull and the scalp. The amount of shunting depends on for example the electrode

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con-14 Theory

figuration. The current is conducted by the CSF and through the CSF network, the current can later pass into the brain of the patient.

(a) 4-by-1 ring configuration. (b) Rectangular pad configuration.

(c) Current flow for 4-by-1 ring config-uration.

(d) Current flow for rectangular con-figuration.

Figure 2.5: Upper row: examples of different electrode configurations (anode in red and cathode in blue). Second row: simulated current flow for the two electrode configurations [12].

Studies have been conducted on which electrode configuration is better, e.g. a ring configuration, seen in Figure 2.5a, or rectangular pads, seen in Figure 2.5b. Using a ring configuration, the treatment has a higher spatial focality since the current does not need to pass across the patient’s brain. Instead only the smaller area of interest is stimulated compared to the rectangular electrode configuration. Illustrations of finite element method (FEM) simulations of the current flow within the patient’s brain using the different electrode configurations can be seen in Figure 2.5c and Figure 2.5d. The results indicate that the ring configuration is superior. Research has also been conducted on the affect of placement of the return electrode for the rectangular configuration, e.g. on the forehead above the contralateral orbita or on the patient’s contralateral shoulder. Electrode montage (size and position) determines the current flow, thus determining the neurophysiological

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2.3 Segmentation 15

effects.

If the 4-by-1 ring configuration with sponges is used for stimulation, the max-imum current used is 2 mA for a maxmax-imum of 22 minutes [13]. If a different electrode setting is used or if the sponges are substituted with a conductive gel, the strength of the field and the stimulation time change. Both direct current and alternating current has been used with a positive result [14].

2.2.1 Transcranial Direct Current Stimulation (tDCS)

tDCS was developed in the early 19th_{century and involves stimulation of the brain}

with constant direct current through scalp electrodes. It is non-invasive, cost-efficient, and the administration is non-complicated [15]. Electrodes are placed on the scalp, usually with a sponge or a gel in between for better conductivity, and a small amount of current is applied for a certain amount of time. Treatment of some patients require repeated stimulation for a number of visits, e.g. once a week for ten consecutive weeks, for optimal result.

The anode is placed on the area of interest, e.g. on the motor cortex for patients with movement disabilities or on the speech center for patients with aphasia. When current is applied, it induces intracerebral blood flow. This leads to a decrease or an increase in neuronal excitability, depending on which type of stimulation is used. An alteration in brain function will be the result. tDCS is used in combination with behaviour therapy to help patients with aphasia to learn to speak again. This method can for example be used on patients with a small vocabulary resulting from the aphasia. For example one of the patients in the study was only able to pronounce one word and was later helped with this method. The behaviour therapy involves naming objects during a functional MRI (fMRI) scan to see which areas of the brain are affected by the stroke. The severity of the aphasia decides how many words the patient is able to say. Information from the fMRI is used to place stimulation electrodes on the head of the patient. One week after the stimulation, many patients are still able to pronounce the words practiced during the therapy, which is a vast improvement. The improvement is mostly due to the behaviour therapy, which is then boosted by the tDCS [16, 17, 18].

2.3 Segmentation

To know how much current to apply during tDCS and at what location to do so, segmentation is the key. By segmenting the brain into different tissue classes, FEM simulation can later predict how current will flow inside the head. The segmenta-tion can for example be based on image intensity, color, or texture and all voxels inside one segmentation region are similar in some way [19]. Since the presence of lesions represents a change in electrical conductivity, proper segmentation is crucial. Instead of normal brain matter, the lesioned part of the brain might only contain CSF which is highly conductive, thus changing the current flow within the brain. If the lesion is acute, there is only a small change in electrical conductivity of the tissue, although this might be enough to give a bad stimulation result and thus, this needs to be considered.

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16 Theory

There are two main methods to use when segmenting the brain; either it can be done manually or automatically. Both techniques have their pros and cons and are described more thouroughly in Section 2.3.1 and Section 2.3.2. There is also a combination of the two methods available, where the brain is first automatically segmented and then some post-processing is performed by an operator, but this is outside the scope of this thesis.

2.3.1 Manual Segmentation

Manual segmentation has long been the gold-standard for lesion identification and according to John Ashburner ’human experts are currently believed to be able to partition a brain image in a more accurate way than automated algorithms’ [20]. The segmentation is mouse-based and involves outlining or filling brain regions for every 2D slice of an MRI scan. Since the user can only look at 2D slices of the brain, it is important to be able to mentally reconstruct a 3D image to ensure continuous tissue classes. Trained personel is required and it is very time consuming [21], a scan currently takes about a few weeks to segment. Due to this time factor, manual segmentation does not serve the needs for daily clinical use.

It is a laborious method that depends on the skill of the operator. When it comes to segmentation of lesioned brains, manual segmentation is more reliable than automatic segmentation since most automatic segmentation routines are not implemented with an extra tissue class for the lesioned tissue.

2.3.2 Automatic Segmentation

Automatic segmentation is a fast technique giving an acceptable result if the pa-tient has a normal brain anatomy without lesions and other abnormalities. Prob-lems can also occur if the patient is older, since the brain shrinks with increasing age and does not correspond to the expected brain anatomy. The automatic seg-mentation is based on a prior probability of finding different tissue types at a certain location in the brain, this probability itself is based on information from normal brains combined with an estimated likelihood of observing the present intensity value. Due to this, automatic segmentation routines seldom produce correct segmentation results for lesioned brains.

In the presence of stroke lesions, the segmentation routine performs badly and misclassifies the lesion as something else believing that there should only be normal tissue at the lesion location. There are many research teams currently conducting studies on how to improve the automatic segmentation routines available on the market today, more information about this can be found in Section 3.

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Chapter 3

Previous Work

The previous work studied involves the SPM8 software package (developed by the Wellcome Trust Centre for Neuroimaging, London, UK, http://www.fil.ion. ucl.ac.uk/spm/) and the proposed changes made on it. SPM8 is the software of choice in this thesis since it is state-of-the-art and widely used within this field. A search in PubMed for articles related to this software was performed and articles with a good theoretical background connected to this thesis work were chosen. Articles from the Wellcome Trust Centre for Neuroimaging’s homepage were also studied to get a better knowledge base on how the software works and the theory behind it.

Modifications to the original segmentation routine in SPM8 have been made the past years, information about these was also studied to get an idea on how the current problems could be solved and implemented. The modifications of choice for this thesis are Chris Rorden’s improved TPMs, the new segmentation routine included in SPM8 (which is only work in progress), and a lesion classification algorithm made by Mohamed Seghier. These automatic segmentation routines were chosen since they have not yet been compared against each other.

The original segmentation routine in SPM8 and the modifications are described more thoroughly in Section 3.1 - 3.4. A description of the manual segmentation is included in Section 3.5 to give the reader an understanding of the technique and possible problems that might occur.

3.1 SPM8 Original Segmentation

SPM8 is the latest version of the statistical parametric mapping (SPM) software. The name refers to the ’construction and assessment of spatially extended statisti-cal processes used to test hypotheses about functional imaging data’ [22]. This soft-ware combines segmentation, bias correction, and spatial normalization through the inversion of a single unified model [23] and runs in Matlab. In brief, the uni-fied model combines tissue classification, intensity bias, and non-linear warping in one probabilistic model. Image intensities are modelled as a mixture of Gaus-sians (MOG) and a prior probability that a voxel intensity is drawn from a certain

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18 Previous Work

tissue class is given by the mixing proportions. In the unified model, the priors of the tissue classes are encoded by deformable TPMs illustrated in Figure 3.1.

(a) Gray matter. (b) White matter. (c) CSF.

Figure 3.1: Illustration of the TPMs used by the original segmentation routine in SPM8. Image intensity values range from zero (black) to one (white). A higher intensity value represents a higher probability of the voxel correponding to the tissue of interest.

The tissue classification requires the image volumes to be registered to the TPMs. After the registration, the maps represent a prior probability of voxels at each location belonging to different tissue classes. Bayes rule is then used to combine these priors with the probabilities for different tissue classes derived from different voxels intensities to provide a posterior probability. However, this procedure is circular since the registration requires an initial tissue classification and vice versa. This problem is solved by combining both components into a single generative model. An example of a segmentation result from the original segmentation routine can be seen in Figure 3.2a.

The segmentation routine automatically segments the image volume into GM, WM, and CSF based upon the probability of finding these tissue types at dif-ferent locations combined with an estimated likelihood of observing the present intensity value. This is done by using the TPMs previously mentioned, one for each tissue class. The TPMs are made by taking the average of a large number of normal brains. The TPMs used in the original segmentation rou-tine are modified versions of the maps provided by the ICBM Tissue Proba-bilistic Atlases. These TPMs are kindly provided by the International Con-sortium for Brain Mapping, John C. Mazziotta and Arthur W. Toga. http: //www.loni.ucla.edu/ICBM/ICBMTissueProb.html.

There are a number of user settings in the original segmentation routine, which when changed will affect the segmentation result. These settings are specified and explained more thoroughly in Section 3.1.1.

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3.1 SPM8 Original Segmentation 19

(a) SPM8 original segmentation. (b) ALI.

Figure 3.2: Example of segmentation result from the original segmentation in SPM8 and ALI for a patient with a necrotic stroke lesion (Keith). GM is marked with a red color, WM is represented by a blue color, and CSF is represented by a green color. For ALI, the lesion class is marked with a pink color. These are the same axial slices as shown in Figure 4.2a. The result is thresholded by 0.2 to remove voxels with a low probability of belonging to the segmented tissue classes.

3.1.1 Settings

The settings for the original segmentation routine in SPM8 can be divided into three main categories; data, output files, and custom. These settings are explained more in detail in Table 3.1.

3.1.2 Problems

The major problem with SPM8 original segmentation is that since the TPMs are based on normal brains only, segmentation of an abnormal brain will give an incorrect result. The segmentation routine only classifies the brain tissue into three different tissue classes, which is a problem if you for example would like to segment out bone tissue. Better classes for internal structures in the GM would give a more accurate segmentation result. Since this is only a mono-spectral implementation, it can be improved by making it multi-spectral by including the information from for example a T2 weighted MRI. The model does not take into account that neighbouring voxels are likely to be of the same tissue class.

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Data This setting involves choosing which image volumes to segment. Currently, this segmentation routine only works in a mono-spectral way, i.e. with infor-mation from only one image modality.

Output files

Gray matter Choose which output file to produce for GM. The options are native space, unmodulated normalized, modulated normalized, and a combination of them. White matter Same options as for GM.

Cerebro-spinal fluid Same options as for GM.

Bias corrected Choose to save a bias corrected version of the input data.

Clean up any partitions Decide if there should be any cleaning up performed on the result, this enables an extraction of the brain from the segmented images.

Custom

Tissue probability maps Choose which TPMs to use during the segmentation. Gaussians per class Choose how many Gaussians that should be used to

represent the image intensity for each tissue class. Affine regularisation Choose which brain type to perform the affine

regu-larisation to.

Warping regularisation The amount of regularisation involves a tradeoff be-tween two terms; how probable the data is given the warping parameters and how probable the parame-ters are including a penalty for unlike deformations. If the image appears distorted, it is a good idea to increase the amount of regularisation. An increased regularisation gives smoother deformations.

Warp frequency cutoff A smaller number will allow more detailed deforma-tions to be modelled, although this gives a great in-crease in memory needed and thus, also computa-tional time is increased.

Bias regularisation Involves a spatial bias that may corrupt the image volume and make segmentation harder. If the data has very little intensity non-uniformity artifacts, the bias regularisation should be increased.

Bias FWHM Setting the full width half maximum (FWHM) of the Gaussian smoothness of the bias. This will pre-vent the algorithm from trying to model out intensity variations due to different tissue types.

Sampling distance Choose sampling distance for estimation of model parameters. Smaller values use more data, thus giv-ing a better segmentation result. Although this will also lead to an increase in computational time. Masking image The result can be masked to ensure that it conforms

the same space as the original image volume. Table 3.1: Settings for SPM8 original segmentation.

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3.2 Mohamed Seghier’s ALI 21

3.2 Mohamed Seghier’s ALI

Mohamed Seghier has improved the original segmentation routine in SPM8 by adding a tissue class for the lesion. He has published an article [21] about this work and was kind enough to provide the software code for evaluation. This has enabled testing and evaluation of the segmentation algorithm on both necrotic stroke lesions and acute stroke lesions.

ALI is a modification of the original unified segmentation already present in SPM8. The purpose of the segmentation routine is to minimize misclassification of abnormal tissue in GM and WM segments by segmenting these abnormal vox-els as CSF and/or as members of a fourth extra tissue class. An example of a segmentation result from ALI can be seen in Figure 3.2b.

With ALI the user can identify abnormal/lesioned brain tissue and generate lesion overlap maps (LOMs) of the population/group. Basically, when lesions are identified, a LOM can be generated and voxels that are frequently lesioned in the population/group can be identified (e.g. this is useful for lesion-symptom mapping). The identification of abnormal brain tissue is divided into four steps.

• Segmentation of the anatomical images with the modified unified segmenta-tion (with an ’extra’ lesion class).

• Smoothing of the segmented GM and WM images.

• Detection of abnormal voxels in both GM and WM images (compared to normal values of a control population).

• Grouping and definition of the lesion.

The prior probability for the lesion class is made up by a combination of the TPMs for CSF and WM. The default setting is the mean of these two TPMs. This simple prior has a low probability in the GM to avoid including normal GM voxels in the lesion class. Similarly, the prior has a low probability in the WM segment. The prior has a higher probability in the WM area than in the GM area, thus forcing the segmentation to reclassify mis-classified tissue. An illustration of the TPM for the extra tissue class can be seen in Figure 3.3.

Figure 3.3: Illustration of the TPM for the extra class used by ALI. Image intensity values range from zero (black) to one (white). A higher intensity value represents a higher probability of the voxel correponding to the tissue of interest.

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3.2.1 Settings

There are not a lot of settings for the user to change in ALI, the settings involving the unified segmentation are set by the program itself. The settings that the user can change are specified in Table 3.2.

Data Involves choosing which image volumes to segment. Important to remember is that ALI is only tested for T1 weighted MRIs.

Tissues

Prior extra class The prior of the extra class is chosen. The default TPM is the mean of the TPMs for WM and CSF. This is a good estimate.

Other settings

Number of iterations Choose how many iterations to run, a good value is around two to three. A lower number of iterations may lead to lesioned tissue not being included in the extra/lesion class. An increased number of iterations may lead to healthy tissue being classified as lesioned tissue. Information about this can be found in the complementary material to [21].

Threshold probability Before using the result as a prior probability in the next iteration, the result is cleaned up by removing voxels below a user defined probability value. This step will help to limit the search to only abnormal voxels with a high probability in the lesion class. Threshold size Before using the result as a prior probability in the

next iteration, the result is cleaned up to remove smaller areas segmented as lesion. Only regions with a relatively big size (> threshold) will be considered for the definition of the prior for the lesion class. Coregister to MNI space Choose to coregister to Montreal Neurological

Insti-tute (MNI) space or not. Coregistration will help the accuracy of the segmentation routine.

Table 3.2: Settings for ALI.

3.2.2 Problems

ALI is a temporary version and is only tested for T1 weighted MRIs. For some patients the lesion is not classified as lesion; if the lesion class is empty after segmentation, either the lesion area is classified as part of another tissue class or not at all.

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3.3 SPM8 New Segmentation 23

3.3 SPM8 New Segmentation

The new segmentation routine in SPM8 is an extension of the default unified segmentation. It is essentially equal to the algorithm described in Section 3.1, although there are four major changes made. First, there is a slightly different treatment of the mixing proportions. Second, the registration model used is im-proved. Third, a multi-spectral mode is implemented, making it possible to use information from different image modalities (e.g. T1 weighted MRIs, T2 weighted MRIs, and diffusion weighted imaging (DWI)) in the segmentation. Fourth, there is an extended set of TPMs, illustrated in Figure 3.4, allowing voxels within the brain to be treated differently.

Figure 3.4: Illustration of the TPMs used by the new segmentation routine in SPM8. Image intensity values range from zero (black) to one (white). A higher intensity value represents a higher probability of the voxel correponding to the tissue of interest.

The algorithm can segment the brain into a total of six tissue classes; GM, WM, CSF, bone, soft tissue, and air/background. An example of a segmentation result from this routine can be seen in Figure 3.5.

3.3.1 Settings

The settings for this segmentation routine are similar to the ones mentioned in Section 3.1.1. The settings are presented and explained in Table 3.3.

3.3.2 Problems

Since this is only an extension of the original segmentation routine in SPM8, the problem with lesioned brains lingers. The new segmentation routine is included in SPM8, although it is only work in progress and there is hardly any documentation on it. Another question is if it is rigourously tested. The multi-spectral mode does not produce a correct segmentation result, e.g. the segmented CSF is not a continuous layer which is anatomically incorrect.

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Data

Volumes This setting involves choosing which image volumes to segment. Firstly, the user decides whether to use mono-spectral mode or multi-spectral mode. The different channels can be treated in different ways when it comes to the bias, although it is only the first channel’s data that is used for the initial affine regularisation with the TPMs.

Bias regularisation Involves a spatial bias that may corrupt the image volume and make segmentation harder. If the data has very little intensity non-uniformity artifacts, the bias regularisation should be increased.

Bias FWHM Setting of the FWHM of the Gaussian smoothness of the bias. This will prevent the algorithm from try-ing to model out intensity variations due to different tissue types.

Save bias corrected Choose whether to save a bias corrected version or not.

Tissues

Tissue probability maps Choose which TPMs to use, in the same way as in Section 3.1.1.

Number of Gaussians Choose which number of Gaussians to use to describe the image intensity of the tissue classes.

Native tissue Decide whether to save the result in native space or not. By saving the segmentation result in the native space, the result can later be overlayed on top of the original anatomical image.

Warped tissue Decide to save the result in warped space or not. If so, decide which warping to use.

Warping

Warping regularisation The amount of regularisation involves a tradeoff be-tween two terms; how probable the data is given the warping parameters and how probable the parame-ters are including a penalty for unlike deformations. If the image appears distorted, it is a good idea to increase the amount of regularisation. An increased regularisation gives smoother deformations.

Affine regularisation Choose which brain type to perform the affine regu-larisation to.

Sampling distance Choose sampling distance for estimation of model parameters. Smaller values use more data, thus giv-ing a better segmentation result. Although this will also lead to an increase in computational time. Deformation fields Select to save the deformation field as a .nii-file or

not.

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3.3 SPM8 New Segmentation 25

(a) SPM8 new segmentation mono-spectral.

(b) SPM8 new segmentation multi-spectral.

Figure 3.5: Example of segmentation results from the new segmentation routine in SPM8 using default TPMs for mono-spectral mode and multi-spectral mode on a patient with a necrotic stroke lesion (Keith). GM is marked with a red color, WM is represented by a blue color, and CSF is represented by a green color. These are the same axial slices as shown in Figure 4.2a. The result is thresholded by 0.2 to remove voxels with a low probability of belonging to the segmented tissue classes.

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3.4 Chris Rorden’s TPMs

Chris Rorden, professor at the Georgia Institute of Technology (www.sph.sc.edu/ comd/rorden/), has improved segmentation performed with SPM8 by providing improved TPMs. There are two different versions of TPMs available, either a set including only the head region or a set that also includes a part of the neck and the spine. There are TPMs available for classification of six tissue classes; GM, WM, CSF, bone, soft tissue, and air/background. In this thesis work, the TPMs for the head region only are used. These TPMs are illustrated in Figure 3.6.

Figure 3.6: Illustration of Chris Rorden’s TPMs. Image intensity values range from zero (black) to one (white). A higher intensity value represents a higher probability of the voxel correponding to the tissue of interest.

The TPMs are used in the new segmentation routine in SPM8 to evaluate in what way segmentation result is improved. Both mono-spectral mode and multi-spectral mode will be used and evaluated. Examples of segmentation results from the new segmentation routine in SPM8 using Chris Rorden’s TPMs are illustrated in Figure 3.7.

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3.4 Chris Rorden’s TPMs 27

(a) SPM8 Chris Rorden

mono-spectral.

(b) SPM8 Chris Rorden multi-spectral.

Figure 3.7: Example of segmentation results from the new segmentation routine in SPM8 using Chris Rorden’s TPMs for mono-spectral mode and multi-spectral mode on a patient with a necrotic stroke lesion (Keith). GM is marked with a red color, WM is represented by a blue color, and CSF is represented by a green color. These are the same axial slices as shown in Figure 4.2a. The result is thresholded by 0.2 to remove voxels with a low probability of belonging to the segmented tissue classes.

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3.5 Manual Segmentation

Manual segmentation is, as mentioned earlier, considered gold-standard and will be treated as the correct way of segmenting the brain in this thesis, i.e. the ground truth. The segmentation is performed by the Neural Engineering group at The City College of New York using a software named ScanIP, a feature of Simpleware. It is performed by a skilled engineer and it currently takes a couple of weeks to segment one image volume into six tissue classes; GM, WM, CSF, skin, bone, and air/sinus.

The operator manually marks voxels corresponding to different tissue classes by clicking on the screen. Each tissue is a different mask and each mask can be set to a different color. This is repeated for all axial slices of the image volume, making it important to have a good spatial thinking while segmenting to for example ensure continuous tissue layers. Another challenge is the quality of the anatomical images which varies between scans. In some cases it can be hard to distinguish between tissue types since there is not a clear line identifying the boundary. In those cases, the segmentation is based more on experience of the operator and the surrounding image slices.

The general procedure involves setting the shape of the head by coloring the skull. The skin is then traced and any overlapping skin is subtracted from the skull. This is followed by a similar procedure for the brain. The folds and shapes of the brain is outlined by marking the CSF and subtracting the brain matter (usually GM) from the CSF.

3.5.1 Problems

Since manual segmentation involves one person going through all slices of a image volume, it is very time consuming. It is also user dependant, hence the segmenta-tion result depends on the skill of the operator. Due to the operator dependancy, segmentation result of the same patient might give a different result when done by someone else.

Since only information from T1 weighted MRIs is used during the manual seg-mentation, there can be an error in segmentation result since not all structures are clearly visible. One idea would be to use information from T2 weighted image vol-umes also to get a more reliable result. For acute stroke lesions, the segmentation may be helped by using information from different image modalities, for example DWIs.

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Chapter 4

Method

To evaluate the performance of the automatic segmentation routines, a number of stroke patients are used as an input. The influence of the settings was tested, although the default settings gave the best results and are used to produce the results in this report. The patients are presented in Section 4.1 which is followed by an introduction to the evaluation technique in Section 4.3.

The image volumes were shifted using SPM8 to ensure that the location of the anterior commissure (AC) is coordinate [0 0 0] and that the line between the AC and the posterior commissure (PC) is horizontal. This is important since the automatic segmentation routines assume this. An illustration of AC, PC, and the line connecting them (AC-PC line) can be seen in Figure 4.1. The segmentation routines also depend on the patient being coregistered to the TPMs. If the seg-mentation result was poor, a coregistration to the TPMs was performed and the patient was segmented again. This was only the case using some of the automatic segmentation routines on the patient named Ela, presented in Section 4.1.1.

Figure 4.1: Illustration of AC, PC, and AC-PC line [24].

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30 Method

4.1 Patients

Automatic segmentation routines are known to perform well in presence of necrotic stroke lesions, since the only difference from a normal brain is the extra presence of CSF at the lesion site. Thus, necrotic stroke lesions are usually just segmented as CSF which is correct. Problems occur when there is scar tissue present at the lesion site, which is usually segmented as GM since the image intensity is similar to that of GM. Since the segmentation is based on the image intensity, this is correct. There will be a problem when using this segmentation result in the FEM simulation though since scar tissue does not have the same electrical conductivity as GM. Brains with acute stroke lesions are known to cause more problems and give a poor segmentation result due to only a slight change in image intensity in the lesion area. Depending on what type of lesion present, it can be segmented as either GM, WM, or CSF depending on the image intensity at the lesion site.

First, the automatic segmentation routines are evaluated for patients with necrotic stroke lesions. After analyzing the segmentation results, the automatic segmentation routines are again evaluated for patients with acute stroke lesions.

4.1.1 Patients with Necrotic Stroke Lesions

A set of four patients with necrotic stroke lesions is used to initially evaluate the segmentation routines. The lesions that are somewhat different from each other, regarding size, location and image intensity, are illustrated in Figure 4.2 and Figure 4.3. Both mono-spectral segmentation and multi-specral segmentation is performed on three of these patients; on Ela only mono-spectral segmentation is performed since only a T1 weighted MRI is available for her.

Keith

Keith has a large lesion, which can be seen in Figure 4.2a and Figure 4.2d, affecting the left hemisphere including the speech center. The lesion is labeled as an open lesion, since it affects the outer part of the cerebral cortex it is impossible to know the outer border of it. There is a mass of scar tissue in the lesion area, seen as a darker image intensity of the tissue surrounding the lesion area (the paralesional area). Since the scar tissue has an image intensity similar to that of GM, there is a high probability of the scar tissue being segmented as GM. Since scar tissue is known to have a slightly different electrical conductivity than GM, the result from the FEM simulation will not be entirely correct.

LeftMca

This patient has a stroke lesion similar to Keith’s, although it affects a larger part of the left hemisphere. This stroke lesion is also classified as open since there is no clear outer border of it. There is a lot of scar tissue present at the lesion site, following earlier reasoning it will probably be segmented as GM. Axial slices of this patient can be seen in Figure 4.2b and Figure 4.2e.

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4.1 Patients 31

(a) Keith T1. (b) LeftMca T1. (c) SmallTemporal T1.

(d) Keith T2. (e) LeftMca T2. (f) SmallTemporal T2.

Figure 4.2: Axial slices from T1 weighted MRIs (first row) and T2 weighted MRIs (second row) of patients with necrotic stroke lesions (lesion area marked with red arrow). Images kindly provided by Julius Fridriksson, associate professor at The University of South Carolina.

SmallTemporal

The stroke lesion, which can be seen in Figure 4.2c and Figure 4.2f, is located in the left hemisphere along the edge of the cerebral cortex in the temporal lobe. It is not as big as the lesions previously mentioned, although there is a change in anatomy and image intensity. The image intensity of the lesion is similar to that of GM, thus there is a high probability of the lesion being segmented as GM.

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32 Method

Ela

Ela has a lesion in the visual cortex, located in the posterior part of the brain. The lesion, illustrated in Figure 4.3, has well defined borders and is filled with CSF. There is a high probability of this stroke lesion being segmented as CSF only.

Figure 4.3: Axial slice from a T1 weighted MRI of Ela with a necrotic stroke lesion (lesion area marked with red arrow). Image kindly provided by The City College of New York.

4.1.2 Patients with Acute Stroke Lesions

A set of three patients, illustrated in Figure 4.4, with acute stroke lesions is also used to evaluate the automatic segmentation routines. The lesions are all different from each other regarding size, location, and image intensity. The MRIs from the patients with acute stroke lesions are of low resolution compared to those with necrotic stroke lesions. The mono-spectral mode was used on these patients since there were no T2 weighted MRIs available.

3316

This patient has a large lesion, seen in Figure 4.4a, in the posterior part of the right hemisphere. The actual lesion is the dark matter surrounded by a white border. This is a result of a hemmorhagic stroke when blood leaks out into the brain. It is hard to know what the white border actually is and if it is a part of the lesion. Since the lesion has a low image intensity similar to that of GM inside the white border, it will probably be segmented as that. The white ring has a very bright image intensity and will probably be segmented as WM.

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4.2 Automatic Segmentation vs. Ground Truth 33

3319

This patient’s lesion, seen in Figure 4.4b, is marked by the white oval shaped area in the right hemisphere. By looking at results from another image modalities than the T1 weighted image (e.g. DWI), it is clear that the lesion site is larger than what can be seen in the T1 image. Due to the intensity of the lesion being similar to that of WM, it will probably be segmented as WM.

3322

The lesion in this patient is a bit different from the other two patients with acute stroke lesions since it is on the outer part of the cerebral cortex. The lesion is illustrated in Figure 4.4c by the area with a lower image intensity. Due to the image intensity of the lesion area, it will probably be segmented as GM.

(a) T1 3316 (b) T1 3319 (c) T1 3322

Figure 4.4: Axial slices from T1 weighted MRIs of patients with acute stroke lesions (lesion area marked with red arrow). Images kindly provided by Julius Fridriksson, associate professor at The University of South Carolina.

4.2 Automatic Segmentation vs. Ground Truth

Ground truth (manually segmented GM, WM, CSF, bone, skin, and lesion) is available for one stroke patient (Ela). Ground truth (manually segmented GM, WM, CSF, bone, and skin) is also available for one healthy patient. A comparison will be made between the automatic segmentation routines and the ground truths for both patients. The original segmentation routine in SPM8, the new segmen-tation routine in SPM8 using the default TPMs, the new segmensegmen-tation routine in SPM8 using Chris Rorden’s TPMs, and Mohamed Seghier’s ALI will be evaluated and the corresponding segmentation results compared to each other.

A confusion matrix is made for each automatic segmentation routine and each patient. A confusion matrix represents the accuracy of the predicted result (from the automatic segmentation routine) compared to the ground truth. An Error

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34 Method

rate and a Total error rate of how well the segmentation routines perform are calculated based on the elements in the confusion matrix. The diagonal elements are considered correct and all off-diagonal elements are considered part of the error.

The Error rate, see Equation 4.1, calculated for each tissue type (T) and seg-mentation routine (M), is a measurement of how well the automatic segseg-mentation routine performs for each tissue class. The Total error rate, see Equation 4.2, which is calculated for each automatic segmentation routine (M), is a measurement of how well the segmentation routine performs over all. The error measurements are calculated as

Error rate(T,M) = error(T, M )

error(T, M ) + correct(T, M ) (4.1)

Total error rate(M) = error(M )

error(M ) + correct(M ) (4.2)

4.3 Regions of Interest (ROIs)

To compare the results from the automatic segmentation routines, a region of interest (ROI) is defined for each patient (except for Ela who is only used in the comparison between automatic segmentation and ground truth). The ROIs are defined as cubes with a side length of 30 voxels. Since the image volumes for each patient have different image resolution, the size of the ROI in millimeter (mm) differs between the patients. This can be seen in Table 4.1. Lesioned tissue is included in the ROIs, along with normal tissue and scar tissue, to evaluate the performance of the routine at the lesion site. An example of a ROI can be seen in Figure 4.5.

Patient Image resolution [mm]

Keith 1 * 1 * 1 LeftMca 1 * 1 * 1 SmallTemporal 1 * 1 * 1 3316 0.45 * 0.45 * 0.9 3319 0.98 * 0.98 * 1 3322 0.98 * 0.98 * 1

Table 4.1: Image resolution for image volumes/patients used to evaluate the au-tomatic segmentation routines.

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4.3 Regions of Interest (ROIs) 35

Voxels corresponding to different tissue classes within the ROI are summarized as a measure of how well the automatic segmentation routines performs. For the patient with manual segmentation results available (Keith), this will be considered ground truth and the automatic segmentation routines will be compared to these values. For the other patients, there is no ground truth available and a comparison between the automatic segmentation routines will be made instead.

Figure 4.5: Keith’s ROI illustrated by a white square. Top left corner coronal slice, top right corner sagittal slice, and bottom left corner axial slice. The ROI is a cube with a side length of 30 voxels (30 mm).

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Chapter 5

Results

This section includes results from the segmentation routines for each patient. The manual segmentation (ground truth) for Ela is compared to the results from the automatic segmentation routines in voxel-by-voxel plots. Confusion matrices are made and error rates are calculated for one stroke patient (Ela) and one healthy patient. The segmentation results from the healthy patient will be considered ideal. Finally, the results from the ROIs are summarized for each patient (three acute and three necrotic) and each segmentation routine. Further analysis and conclusions of the results can be found in Section 6.

5.1 Automatic Segmentation vs. Ground Truth

A comparison is made between the automatic segmentation results and the manual segmentation results (ground truth) for Ela. Illustrations of the results can be seen in Figure 5.1 - 5.4, where the difference in segmentation result is plotted voxel-by-voxel.

Confusion matrices are made for each automatic segmentation routine and each patient (one stroke patient (Ela) and one healthy patient). The confusion matrices represent the accuracy of the automatic segmentation results compared to ground truth. The confusion matrices are illustrated in Figure 5.5 and Figure 5.6. All confusion matrices for Ela are normalized in the same manner. This is also true for the healthy patient, although the confusion matrices are normalized with a different value than for Ela.

An Error rate is calculated for each segmented tissue class and each automatic segmentation routine. It is summarized in plots which can be seen in Figure 5.7a and Figure 5.7b. The error rate is normalized in the same manner for all tissue types for Ela. This is also true for the error rate for the healthy patient, although it is normalized with a different value than for Ela. A Total error rate is calculated for each automatic segmentation routine. It is summarized in plots which can be seen in Figure 5.7c and Figure 5.7d.

The image intensity of the confusion matrices and the error plots is gamma corrected (γ = 0.5) for both the healthy patient and the stroke patient. This

Stroke Lesion Segmentation for tDCS

Stroke Lesion Segmentation for tDCS

Stroke Lesion Segmentation for tDCS

Examensarbete utfört i medicinsk informatik

vid Tekniska högskolan i Linköping

av

Abstract

Acknowledgments

Contents

List of Figures

Acronyms

Chapter 1

Introduction

1.1

Problem Formulation

1.2

Related Research

1.3

Purpose and Goals

1.4

Organization of the Thesis Report

Chapter 2

Theory

2.1

The Human Brain

2.1.1

A Healthy Brain

2.1.2

Strokes

2.1.3

Stroke Lesions

2.2

Stimulation of the Brain

2.2.1

Transcranial Direct Current Stimulation (tDCS)

2.3

Segmentation

2.3.1

Manual Segmentation

2.3.2

Automatic Segmentation

Chapter 3

Previous Work

3.1

SPM8 Original Segmentation

3.1.1

Settings

3.1.2

Problems

3.2

Mohamed Seghier’s ALI

3.2.1

Settings

3.2.2

Problems

3.3

SPM8 New Segmentation

3.3.1

Settings

3.3.2

Problems

3.4

Chris Rorden’s TPMs

3.5

Manual Segmentation

3.5.1

Problems

Chapter 4

Method

4.1

Patients

4.1.1

Patients with Necrotic Stroke Lesions

4.1.2

Patients with Acute Stroke Lesions

4.2

Automatic Segmentation vs. Ground Truth

4.3

Regions of Interest (ROIs)

Chapter 5