Planning safe trajectories in image-guided keyhole ... · Section 1.5 provides a brief overview of the method and the research goals. Section 1.6 discusses the novel aspects of the

Planning safe trajectories in image-guided

keyhole neurosurgery

A thesis submitted in fulfillment of the requirements for the degree of

Master of Science

By

Miri Trope

Supervised by

Prof. Leo Joskowicz Dr. Ruby Shamir

Dr. Yigal Shoshan

The Selim and Rachel Benin

School of Engineering and Computer Science The Hebrew University of Jerusalem

Jerusalem, Israel

July 2012

2

Acknowledgements

I would like to thank the important people who helped me with this thesis. First, I deeply thank

my thesis supervisor Prof. Leo Joskowicz for the opportunity to work on such an interesting

innovative topic and for the guidance, the trust and confidence that you have shown in me during

our work together. I thank Dr. Ruby Shamir for his generosity in donating his helpful ideas and

sharing his knowledge in algorithms and neurosurgery. I also thank Dr. Yigal Shoshan for his

noble attitude in developing a method that significantly improves the skills of neurosurgeons, his

unlimited time for our meetings and for motivating his team to our experiments: Dr. Zvi Israel,

Dr. Idit Tamir, Dr. Frenando Ramirez and Samuel Moscovici. I also thank my colleagues Achia

Kronman and Refael Vivanti at the computer aided surgery and medical image processing

laboratory, and all the other members of the laboratory, who were very informative, hands on

helpers and supportive during the whole way.

I would like to thank my parents, Hana and Moshe Trope, for their faith in me and allowing me to

be as ambitious as I wanted. It was under their watchful eye that I gained so much drive and an

ability to tackle challenges head on. This work is as much yours as it is mine.

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Abstract

Many image-guided keyhole neurosurgery procedures require the precise targeting of tumors and

anatomical structures with a surgical tool inside the brain based on pre-operative CT/MRI images.

A misplacement of the surgical tool from the planned trajectory may result severe neurological

complications. Consequently, it is desired to select a trajectory that is located at a safe distance

from critical structures such as blood vessels, ventricles and some of the major functional areas of

the cerebral cortex which are correlated to the motor, sensory, vision and speech activities and are

represented by bundle of fibers and functional MRI.

We present a novel preoperative straight trajectory planning method that calculates the safest

trajectory of the region of interest in the head surface for image-guided keyhole neurosurgery.

Our method quantifies the risks of multiple candidate trajectories and presents them due to their

associated risk on a color coded head surface to assist the neurosurgeon in selecting the safest

path.

This thesis presents a new software platform including an automatic algorithm for the safest

trajectory planning and brain structures segmentation. For visualization, a color coded head

surface and safest trajectories are presented to assist the neurosurgeon based on a defined target

and a candidate entry point area on the outer head surface on preoperative MRI scans. The

software provides a friendly user interface suitable for the clinical applications including plugins

for each brain structure segmentation, trajectories calculation and evaluation methods.

A retrospective comparative study for a selected target on MRI head scans for five patients

showed a significant reduction in insertion trajectory risk. The safety of our method was

compared with three trainees and a senior neurosurgeon yielding excellent improvement of the

trajectory risk value of less than 25% of the manual trajectories risk values and a minimum

distances difference of 1.6 mm.

The suggested method may result in safer trajectories for complex cases with internal targets,

shorter preoperative planning time and accurate placement of the surgical tool with the farthest

distance from the important brain structures while avoiding possible complications in keyhole

neurosurgery.

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Contents

1. Introduction ………………………………………………………………………..……….…..6

1.1. Background ………………………………………………………………………..….…….6

1.2. Clinical procedures ………………………………………………..…………………..……7

1.3. Keyhole neurosurgery workflow …………………………………………………………...8

1.4. Support system.………..……………………………………………………….…….....…..9

1.5. Thesis overview.……………………………………………………………………....…...13

1.6. Novel aspects.…………………………………………………………………….………..14

1.7. Thesis organization………………………………………………………………...………14

2. Literature review…………………………………………………………………….…...........15

2.1. Planning a safe trajectory in keyhole neurosurgery………………………………...….....15

2.2. Blood vessels segmentation………………………………………………………..…..…..16

3. Method……………………………………………………………………………….………… 9

3.1. Overview…………………………………………………………………………..….…… 9

3.2. Structures segmentation……………………………………………………..…..………... 21

3.3. Calculating a risk map…………………………………………………………..…...……..29

3.4. Calculating and visualizing safe trajectories…………………………………..………..... 31

3.5. Graphical user interface…………………………………………………………..……...…32

4. Experimental results……………………………………………………………….…..…........35

4.1. Datasets description…………………………………………………………………......….35

4.2. Methodology……………………………………………………………….…..…….….….35

4.3. Results……………………………………………………………………….….…..…...…36

5. Conclusion……………………………………………………………………….…..……….…42

5.1. Summary……………………………………………………………………….…..…….…42

5.2. Discussion…………………………………………………………………….……..……...42

5.3. Contribution………………………………………………………………….…..………....45

5.4. Limitations………………………………………………………………….……..…….….46

5.5. Future work………………………………………………………………….……..….……46

6. Bibliography…………………………………………………………………….………..….….47

5

List of Figures

1.1 Stereotactic frame 10

1.2 Navigation system 10

1.3 Robotic neurosurgery system 12

1.4 Interventional MRI 13

1.5 Probe eye view: manual versus automatic methods 14

3.1 Flow diagram of the trajectories planning system 20

3.2 Skull segmentation 22

3.3 Ventricles segmentation 23

3.4 Blood vessels segmentation 25

3.5 Segmented functional areas 26

3.6 Fibers bundle 27

3.7 High density fibers segmentation 28

3.8 Risk map 30

3.9 Color coded trajectories and head surface 32

3.10 Segmented structures and color coded head surface 32

3.11 Planning Trajectories - user interface 33

3.12 MITK platform diagram 34

4.1 Internal deviation of the brain, coronal view 36

4.2 Color coded head surface and trajectories for each patient 41

5.2 Comparison of trajectories risk values – manual versus automatic methods 44

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Chapter 1

Introduction

This thesis presents a method for planning safe insertion trajectories of a straight surgical tool in

keyhole neurosurgery. This chapter introduces the topics of the thesis. In section 1.1, we discuss

the background. In section 1.2, we describe the clinical procedures of the image-guided keyhole

neurosurgeries. In section 1.3, we briefly describe image-guided keyhole neurosurgery

procedures and characterize their commons. In section 1.4, we introduce support systems that

allow the implementation of these operations. Section 1.5 provides a brief overview of the

method and the research goals. Section 1.6 discusses the novel aspects of the method and Section

1.7 discusses the organization of the thesis.

1.1 Background

Neurosurgery is an invasive operation of the brain and spinal cord that its goal is to treat and

diagnose neurological disorders. The extent of the invasion is directly related to the operation

risk. Brain surgeries, in which a large part of the skull is removed, or which involve extensive

surgical intra-cranial intervention, are associated with higher complications and mortality rates

than those performed with a small amount of invasiveness. The development of better imaging

modalities and stereotactic surgery allows applying a minimal invasive approach in which a

neurosurgery is at first planned on the patient’s MRI/CT image to minimize the invasiveness

level of the intervention.

The minimal invasive operation is executed in the operating room using a support system.

Usually, the operation consists of a straight surgical tool inserted through a small hall on the

skull. These types of surgeries, called image-guided keyhole neurosurgeries, are the focus of this

thesis. The systems that support image-guided keyhole neurosurgery bridge between the

preoperative plan and the physical head, and allow the accurate execution of the preoperative

plan on the patient. Since instrument misplacement may result in ineffective treatment and/or

severe neurological complications, it is important to study and improve the systems accuracy and

safety.

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1.2 Clinical Procedures

We briefly present the aim and characteristics of various image-guided keyhole neurosurgeries.

They all have four important common properties:

1) They are minimally invasive surgeries (MIS), performed via a keyhole of 3-30mm diameter

opened on the skull.

2) They require precise targeting and mechanical guidance support.

3) The targets and entry points are determined preoperatively in a CT/MRI image.

4) It is assumed that little or no brain shift occurs due to the minimal invasive approach.

We describe the most common procedures next.

Brain biopsy

Brain biopsy is the gold standard for accurately determining tumor pathology. It consists of

harvesting, with a hollow needle a tissue sample, from a predefined target site within the brain so

it can be analyzed in the pathology laboratory. Biopsies are usually done making a small opening

(3-14mm burr hole) in the skull and carefully introducing a biopsy needle with a support

guidance system.

Hydrocephalus treatment

Treatment of congenital or acquired hydrocephalus is indicated for alleviating the abnormal

accumulation of cerebrospinal fluid (CSF) within the brain ventricles. It consists of diverting the

flow of fluid away from the ventricles by inserting a shunt. A valve in the shunt maintains the

CSF at a normal pressure and volume within the ventricles.

Hematoma evacuation

Evacuation of hematoma is a surgery used to reduce intracranial pressure caused by an

expanding bleeding resulting from head injury stroke, or bleeding into a tumor. The goal of the

surgery is to decrease morbidity and mortality and to relieve neurological symptoms. It consists

of placing an aspiration needle in the hematoma and aspirating the blood out.

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Ommaya catheter insertion

‘Ommaya’ catheter insertion surgery is indicated for the on-site repeated delivery of drugs (e.g.,

chemotherapy and antibiotics) to the ventricular system. It is also performed for repeated CSF

sampling and to evacuate cystic lesions in the brain when surgical excision is not appropriate.

The surgery consists of inserting a silicon catheter into the ventricles or cystic cavity. The

catheter is connected to a small silicon reservoir (the ‘Ommaya’ reservoir) implanted under the

scalp so that the reservoir can then be easily approached with a needle puncture through the

scalp.

Deep brain stimulation

Deep brain stimulation (DBS) is a procedure for effectively treating certain types of

Parkinsonism, primary tremor, dystonia, hemibalismus, and thalamic pain. It consists of

implanting, through one or more small skull openings, electrodes into specific targets of the brain

and providing electric stimuli to these locations.

Minimal access craniotomy

Minimal access craniotomy is performed to resect deep brain tumors, vascular malformations,

and brain abscesses. It consists of opening a small circular hole (20-30mm radius) in the skull

and introducing a surgical instrument reach the area of interest.

1.3 Keyhole neurosurgery workflow

All clinical procedures follow a similar surgical workflow that is described in Table 1 [1].

Preoperatively, markers for image-to-patient alignment may be attached to the patient (1a). Then,

the patient head is imaged (1b) and the neurosurgeon selects the preferred target and entry point

(1c). In the operating room, the patient is prepared for the operation and the support system is

installed (2a). Then, the head image is aligned (e.g. registered) with the actual patient head (2b).

Then, the neurosurgeon localizes the entry point and makes the burr hole (2c). A mechanical

guidance is installed and adjusted to support tool’s insertion (2d), and then, the tool is inserted

and operation is performed. If undesired complication has occurred, steps 2b-e can be repeated as

needed.

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Table 1.1: Typical keyhole image-guided neurosurgery protocol.

1.4 Support Systems

We use this protocol to compare current support systems. Four types of support systems for

minimally invasive keyhole neuro-surgery are currently available: 1. stereotactic frames; 2.

navigation systems; 3. robotic systems, and; 4. interventional imaging systems.

Stereotactic frames

Stereotactic frames (Fig. 1.1) provide precise positioning with a manually adjustable frame

rigidly attached to the patient skull. Prior to image acquisition, four frame position screws are

implanted in the patient’s skull. An imaging coordinate box, called indicator, is mounted on the

frame and the patient is scanned with it. The surgeon identifies the brain targets and entry points

on the images and computes the corresponding stereotactic frame coordinates based on the

imaged indicator. Intra-operatively, the stereotactic frame is adjusted according to the computed

coordinates and mounted on the immobilized patient skull at the implanted screws. Keyhole

surgery of the skull opening is then done. Optionally, a linear drive needle insertion guide is

mounted on the frame to automate needle insertion and retraction.

1. Pre-operatively

1a. Pre-imaging preparations – implant skull screws and/or attach skin markers.

1b. Image acquisition – acquire a CT/MRI image.

1c. Planning – elaborate the pre-operative plan and identify targets and entry points.

2. Intra-operatively

2a. Preparation – set up the support system and perform patient preparation.

2b. Registration – align the preoperative plan and image with the physical head.

2c. Localization – locate the entry point with a tracked tool and perform incision.

2d. Guidance – provide mechanical guidance for the needle/probe insertion.

2e. Insertion – insert the needle to a planned depth at the proper speed/force.

2f. Repeat steps 2b-e as necessary.

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(a) (b)

Figure 1.1: A stereotactic frame: preoperative (a) and intra-operative (b) setups.

(a) (b)

Figure 1.2: A navigation system allows (a) the manipulation of a tracked surgical tool with (b)

real-time localization feedback on the preoperative patient’s head image.

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Navigation systems

Navigation systems (e.g., Medtronic, USA and BrainLab, Germany) show in real time the

location of hand-held tools of the pre-operative image onto which targets have been defined

(Figure 1.2). The registration between the preoperative data and the patient is performed via skin

markers affixed to the patient’s skull before scanning, or by acquiring points on the patient’s face

with either a laser probe or direct contact. Augmented with a manually positioned tracked

passive arm (e.g., Phillips EasyTaxis™ or Image-Guided Neurologics Navigus™) they also

provide mechanical guidance for targeting. Since nearly all navigation systems use optical

tracking, careful camera positioning and the maintenance of a direct line of sight between the

camera and tracked instruments is required at all times. Their disadvantages are: 1) cost (at least

US$150,000); 2) require head immobilization; 3) require the maintenance of a line of sight

between the camera and the instruments, and; 4) require manual passive arm positioning, which

can be time-consuming and error-prone; The main advantages of navigation systems are:1)

provide continuous, real-time surgical tool location information with respect to the defined

target; 2) allow the selection of new target points during surgery; 3) are quickly gaining wide

clinical acceptance since their introduction in the early 1990s.

Robotic systems

Robotic systems (Figure 1.3) provide frameless stereotactic neurosurgery with a robotic arm that

automatically positions itself with respect to a target defined in the preoperative image. They

have the potential to address intra-operative localization, guidance, and insertion with a single

system. The registration between the pre-operative image and the intra-operative situation is

done by direct contact or with video images. Two floor standing commercial robots include the

NeuroMate™ (Integrated Surgical Systems, USA – now defunct) and the PathFinder™

(Armstrong HealthCare, UK). Their disadvantages are: 1) bulky and cumbersome; 2) pose a

potential safety risk due to their size and weight; 3) require head immobilization or real-time

tracking; 4) costly (US$300,000 -$500,000); 5) not commonly used, with only a dozen systems

currently deployed. Their advantages are: 1) provides a frameless integrated solution; 2) allow

intra-operative plan adjustment, and; 3) are rigid and accurate.

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Figure 1.3: Robotic neurosurgery system

Interventional imaging

Interventional imaging systems (Figure 1.4) produce images showing the actual needle/probe

position with respect to the brain anatomy and target. A few experimental systems also

incorporate real-time tracking (Stereotaxis, Inc) and robotic positioning devices. The main

advantage of these systems is that they provide up-to-date images that account for brain shift (a

secondary issue in the procedures we are considering), and needle bending. The main drawbacks

are: 1) limited availability; 2) cumbersome and time-consuming intra-operative image

acquisition; 3) high nominal and operational costs, and; 4) for intra-operative MRI, complete

expensive room shielding is required.

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Figure 1.4: Interventional MRI. (Image source: Univ. of California, Irvine, Dept. of

Neurosurgery website).

1.5 Thesis overview

We present a novel preoperative straight trajectory planning method for image-guided keyhole

neurosurgery. Our method quantifies the risks of multiple candidate trajectories and presents

them on the outer head surface to assist the neurosurgeon in selecting the safest path. For

visualization, we color-code all the trajectories according to their associated risk level and

present them all at once on the relevant parts of the outer head surface.

Uniquely, our approach is from the clinic to the method. That is, at first we characterize the

actual tool localization accuracy under the full clinical setup and in-vivo. Then, we develop and

test methods for improving safety. Figure 1.5 shows our goal in a visual aspect: avoiding

trajectories which intersect with blood vessels, ventricles and functional areas and fibers bundle.

We describe new methods to improve surgical tool placement accuracy and patient safety. The

methods were tested in-vivo and under the full clinical setup, and significant improvements in

accuracy and safety were observed.

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Figure 1.5: Probe eye view- manual versus automatic method

Trajectory which intersects motor (blue) and sensory (purple) areas (left) and the calculated

safest trajectory (right).

1.6 Novel aspects

This thesis is unique in its comprehensive approach combining clinical in-vivo experiments data

with novel technical and theoretical work into a set of methods showed to actually improve the

accuracy and safety in image-guided keyhole neurosurgery.

Our method for minimal-risk path planning was designed carefully with the neurosurgeon such

that safe trajectories can be conveniently selected and refined with a visual and quantitative

feedback. This method is unique in its visualization, risk formula, updated quantitative feedback

interface, and in incorporating tool localization uncertainty model. Moreover, a neurosurgeon

tested the method on clinical data and it was shown that trajectories planned with our method are

safer than those planed with the routine method.

1.7 Thesis organization

The thesis is organized as follows. Chapter 2 presents a survey of planning safe insertion

trajectories of a straight surgical tool in keyhole neurosurgery and brain’s blood vessels

segmentation techniques. Chapter 3 provides an overview of the new method. Chapter 4

describes the experimental validation and presents the results. Chapter 5 concludes with a

summary of contributions, limitations and possible future work.

All our methods were tested under the full clinical setup and showed to improve accuracy and

safety in image-guided keyhole neurosurgery.

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Chapter 2

Literature review

This chapter reviews methods for preoperative planning in keyhole neurosurgery and blood

vessels segmentation. Section 2.1 provides a brief summary of recent studies describing planning

a safe insertion trajectory of a straight surgical tool in keyhole neurosurgery (Table 2.1). Section

2.2 reviews methods for blood vessels segmentation.

2.1 Planning a safe trajectory in keyhole neurosurgery

Vaillant et al [5] quantify the risk of a candidate insertion trajectory based on the sum of the

intensity values of critical brain structure voxels that are intersected by the insertion trajectory,

weighted by their associated importance. The drawback of this approach is that it ignores the

distance of critical structures from the insertion trajectory, so the relative potential for incorrect

surgical tool placement is factored into the risk calculation. Lee et al [4] describe a method that

fuses MRI head scans with a registered atlas to support manual trajectory selection based on 3D

visualization of brain structures. Its main drawback is that the insertion trajectory is selected

manually without any quantitative information regarding nearby critical structures for a specific

patient. Tirelli et al [2] describe a method that assigns a risk value to each candidate insertion

trajectory based on a weighted sum of various factors. Its drawbacks are that it provides a single

risk measure for each trajectory and there is no risk visualization. Brunenberg et al [3] observe

that weighted sum techniques for computing trajectory risk can be misleading, and propose to

compute a maximum risk value for each voxel based on the Euclidean distance of the trajectory

from critical brain structures. Their method produces tens to hundreds of trajectories whose

distances are above a predefined safety threshold. Although this method significantly reduces the

number of candidate insertion trajectories, it still leaves a considerable amount of manual work

to the surgeon, without quantitative feedback for trajectory selection. Navkar et al [1] describe a

method that takes into account the minimum acceptable distance between an insertion trajectory

and the closest blood vessel, as well as the maximum allowed insertion trajectory length, and

shows safe entry point zones on the outer head surface with respect to these criteria. However, as

16

in Brunenberg et al [3] the method requires a considerable amount of manual work from the

neurosurgeon and does not provide quantitative feedback. Note that none of the above studies

quantify trajectory risk reduction or time that may be saved with a more automated selection

process in comparison with manual trajectory selection. Ruby et al [6] describe a method which

quantifies the risk of candidate insertion trajectories on the surgeon defined target and candidate

entry point areas on the outer head surface extracted from preoperative MRI scans. For

visualization and ease of selection, the candidate entry points in the outer head surface areas are

color coded due to their risk. Their method presents as an output just the safest trajectory,

although further calculations can present the safest trajectory in each region on the surface head.

Our method planes safest trajectories by segmenting structures such as blood vessels, ventricles,

functional MRI and high density fibers bundle, calculating a risk map which is the weighted sum

of all those structures and computing for each optional entry point the summarized risk value.

The safest entry points are selected from each region on the surface head and for visualization a

color coded trajectories and head surface are presented.

2.2 Blood vessels segmentation

Vessels segmentation is of great importance, since every organ in the human body has blood

supply. We can divide blood vessels segmentation algorithms into three main categories:

(1) Pattern recognition methods: In the vessel extraction domain, pattern recognition

techniques are concerned with the automatic detection of vessel structures and the vessel

features. Chwialkowski et al [7] preformed contour detection approach, using multi-

resolution analysis based on wavelet transform. Tozaki et al [8] used a skeleton-based

approach which extracts the bronchus and blood vessels from CT scans of the lung. As a

first step, a threshold was used to segment the scans. Then, blood vessels and bronchus

were differentiated by using their anatomical characteristics. Finally, a 3D thinning

algorithm was applied to extract the centerline, which gave the skeleton of the blood

vessels. The resulting skeleton was used to analyze and classify the blood vessels.

Region growing methods can also be considered as pattern recognition techniques.

Schmitt et al [9] added to the growing regions techniques a cavity filling process to add

the cavities missed during seeded region growing process.

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The most popular pattern recognition approach is matching filters. Matching filters

approach convolves the image with multiple matched filters for the extraction of objects

of interest. Sato et al [10] introduces a 3D multi scale line enhancement filter for the

segmentation of curvilinear structures in medical scans. The 3D line filter was based on

the directional second derivatives of smoothed scans using Gaussian kernel using multi

scales with adaptive orientation selection using the Hessian matrix.

(2) Model based methods: In model based methods, explicit vessel models are used to

extract the vasculature. This can include wide spectrum of models, from deformable

models, or active contour to template matching. Kass et al [11] were the first to use active

contour models to extract vessel boundary. The local model, with variable stiffness

parameters, locates the smooth at the location where edge are missing. Edge segments are

extracted using directional gradient information. Level sets methods can also be classified

as model based approach. Caselles et al [12] use propagating interfaces under a curvature

dependent speed function to model anatomical shapes using level sets method. Krissian

et al [13] developed a multi scale model: first, they created a skeleton from local

maximum image, and then extended the skeleton to a full segmentation by using a new

response function which measures the contours of the vessels around the centerlines.

(3) Tracking based methods: Tracking based methods apply local operators on a region

containing a vessel and track it. Starting from an initial point, they detect vessel

centerlines or boundaries by analyzing the pixels orthogonal to the tracking direction. A

sophisticated approach on vessel tracking is the use of graph representation as described

by Freiman et al [14]. The segmentation process is reduced to finding the optimum path

in a graph representation of the aortic arch and carotids arteries from CTA scans. The

method starts with morphological based segmentation of the aorta and the construction of

a prior intensity probability distribution function for arteries. The carotid arteries are then

segmented with graph min-cut method based on a new edge weights function that

adaptively couples the voxel intensity, the intensity prior and geometric vesselness shape

prior.

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METHOD

Risk

computation

method

Multiple

trajectories risk

visualization

Multiple risk

parameters

computation

Safety zone

sleeve

computation

Experimental

validation

methodology

Vaillant

et al. 5

weighted sum yes no no qualitative

Lee et al. 4

No no no no qualitative

Tirelli et

al. 2

weighted sum no no no partially

quantitative

Brunenberg

et al. 3

Maximum

yes

partial

no

qualitative

user evaluation

partially

quantitative

Navkar et

al. 1

maximum +

trajectory

length

yes no no qualitative

user evaluation

partially

quantitative

Ruby et al. 6 maximum +

weighted sum

yes yes yes qualitative

user evaluation

quantitative

Our Method summation +

weighted sum

yes yes yes qualitative

user evaluation

quantitative

Table 2.1: A comparison of straight insertion trajectory preoperative planning methods for

image-guided keyhole surgery. The methods are compared with respect to five categories: (a) the

insertion trajectory risk computation method; (b) multiple trajectory risk visualization; (c)

multiple risk parameter computation, including insertion trajectory length, distance to closest

blood vessel, etc.; (d) insertion trajectory safety zone and its visualization, and; (e) experimental

validation, where the term qualitative indicates visual inspection to evaluate insertion trajectory

safety, user evaluation indicates that the user opinion was reported, and quantitative indicates

that quantitative comparative insertion trajectory risk parameters are reported. Partially

quantitative indicates that only one or two measures were obtained on one image, or that the

surgeon was not involved in the experiment.

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Chapter 3

Methods

This section describes the method for automatic planning of safe trajectories in neurosurgery.

Section 3.1 provides a brief overview of the workflow. Section 3.2 describes the segmentation

method of each structure in the brain. Section 3.3 discusses the risk map calculation and

weighing procedure. Section 3.4 analyses the algorithm for calculating and planning safe

trajectories. Section 3.5 describes the graphical user interface and the interactive medical image

processing software platform.

3.1 Overview

Our preoperative planning system consists of brain internal structures segmentation, calculating

and visualizing trajectories and preoperative planning - a graphical user interface for calculating

automatic trajectories. Our planning method from MRI T1 with gadolinium, functional MRI and

diffusion MRI consists of three steps (Fig. 3.1): The first step is brain internal structures

segmentation such as the ventricles, blood vessels, fMRI and high density fibers. The second

step is calculating risk map due to ranking and weighing process. The third step is calculating

and visualizing color coded trajectories and head surface. The user interface enables the

neurosurgeon to define a target point and number of required trajectories and as an output to

visualize the color coded head surface and trajectories.

Workflow

We propose the following five step preoperative workflow for planning an image-guided keyhole

neurosurgery (Fig. 3.1). (1) MRI datasets are uploaded to the planning station, and the

neurosurgeon defines the target location. (2) Brain structures, e.g. external head skin, blood

vessels, ventricles, functional areas and fibers bundles that the neurosurgeon considers relevant

to the surgery are identified, and are automatically or semi-automatically segmented. (3) Risk

volume is computed by automatically combining the weighted risk values of relevant voxels in

the MRI datasets. (4) Candidate trajectories between the possible entry points and the target are

automatically computed. (5) Candidate entry points on the predefined head outer surface area

and calculated trajectories are color-coded by risk value and displayed to the surgeon.

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Figure 3.1: Flow diagram of the trajectories planning system

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3.2 Structures segmentation

Skull

The skull was made by an automatic segmentation method: all the external non-zero pixels of the

brain were extracted in each axis of a three dimensional MRI image. When the first non zero

pixel was discovered, the last encountered pixel was added to the segmentation.

Delaunay method was calculated in order to generate optimal triangulation. That is, the

maximum interior angle of any triangle is less than or equal to that of any possible triangulation.

One way for detecting if a point D is within a triangle’s circumcircle of A, B, C is to evaluate the

determinant:

> 0

Smoothing was achieved by determining normals for each polygon and averaging them at shared

points. When sharp edges are presented, the edges are splitting and new points are generated to

prevent blurry edges. Shading was achieved by Gouraud’s shading method: the intensities at the

edge of each mesh’s line are calculated from the vertex intensities and the intensities along it.

The interpolation equation is as follows:

The intensity of one pixel can be calculated from the previous pixel according to the increment

of intensity.

ROI extraction

The region of interest had to be extracted from the entire head surface in order to optimize the

planning trajectories algorithm and minimize the run time. Thus the right or left hemisphere was

expelled due to the tumor location and the cerebellar tentorium was used as a lower edge.

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Figure 3.2: Skull segmentation

The red line on the MRI orthogonal slices illustrates the mesh representation of the head surface.

Ventricles

The segmentation method for the ventricles is semi automatic by the seeded region growing

which defined as:

While S = is the set of initial seeds, T is the set of un-allocated pixels which border at

least one of the regions and N(x) is a set of immediate neighbors of pixel x.

We chose that method because as the histogram of the ventricles image shows (Fig. 3.3), there

are two main picks: (1) ventricles and (2) Substantia grisea which is known as the gray matter

and is represented by the medium gray value.

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(a)

(b)

(c)

Figure 3.3: Ventricles segmentation

MRI slice (a) with the segmented ventricles (green) and tumor (purple) showing small frame

of ventricles (right), its histogram (b) and a three dimensional ventricles model (c).

24

Blood vessels

The blood vessels segmentation was complicated due to the partial volume affect and over

segmentation as a result of other pixels with high intensity values. As a solution for those

problems we decided to use the Vesselness algorithm which considers the longitude structure

and the high contrast of the dark background and the bright vessels (thanks to the gadolinium).

The majority of the vesselness algorithm is based on the analysis of eigenvalues of the Hessian

matrix of image intensity. The mutual magnitude of eigenvalues is indicative of the shape of the

underlying object. Isotropic structures are associated to eigenvalues having a similar non-zero

magnitude, while vessels present one negligible and two similar non-zero eigenvalues.

Recalling Frangi’s formulation and indicating the eigenvalues of a Hessian matrix as , and

, with , vesselness is defined in 3D as

And α, β, γ are user defined parameters. The constraints on the sign of eigenvalues assume that

vessels to enhance are bright on a dark background. Thus we observed in the output probability

image the compensation for a non cylindrical structure in areas with a high contrast and a manual

correction was required.

25

Figure 3.4: Blood vessels segmentation

Functional MRI

The functional MRI matrices were achieved from the functional imaging lab in Hadasa Ein

Karem. The functional areas that we used are: speech, motor, sensory and vision. The extraction

process includes of two main stages: the first stage is merging functional areas from different

activities but same functionality (e.g.: functionality: speech, different activities: visual verb

generation and auditory) and the second stage is assigning a threshold value for a probability

image which represents the probability for a functionality in this observed area.

A threshed image can be well produced from the best fitting threshold value definition which

would yield a non spreading spots in the segmented functional areas image.

26

Figure 3.5: Segmented functional areas

Four main functional areas: motor (red), sensory (blue), speech (green) and vision (yellow).

High density fibers

A fibers bundle represents the neuronal tract and was processed by the diffusion MRI imaging.

This section includes of three processes: the first process is converting three dimensional points

in a matrix into polygonal lines that represent a fibers bundle. The second process is generating

an intensity image which represents the fibers density in each voxel. The third process is

thresholding the tract density image in order to achieve a segmented high density fibers image.

Fibers as polygonal lines

Each matrix represents a different functionality and includes of three dimensional points.

The following table presents the fibers bundle composition (from bottom to top):

Structure Type

3D points point

Fiber Line

Bundle of fibers Bundle of lines

In order to achieve a mesh representation each point has to be inserted correspondingly to its

fiber in the corresponding bundle.

27

Figure 3.6: Fibers bundle

Representation of the Superior Longitudinal Fasciculus (SLF), a language-related fibers bundle.

Each fiber is colored due to its gradient. The Broka (green) and Wernicke (blue) functional areas

(fMRI) are also language-related sending neuronal massages by the SLF fiber tracts.

The fibers density image

The fibers density image is a normalized probability image that was calculated due to the

intersected number of fibers in a voxel and normalized by the highest number of fibers in a

voxel. The density value at the midpoint is determined by trilinear interpolation

from the eight nearest nodes on the mesh as follows:

Where are the coordinates of the eight nearest grid points.

With that probability image (Fig. 3.7b, left) we were able to estimate the appropriate threshold

which extracts the high density fibers. That threshold factor is extracted from the manipulated

histograms of the tract density image by the following four steps: the first stage is ejecting the

zero values pixels (the background) and calculating the initial histogram (Fig 3.7a, upper left).

The second stage is converting that histogram into a convenient logarithmic scale. The third

stage is smoothing by Gaussian and the last stage is thresholding by the median value twice. In

average, the number of segmented pixels was reduced by six times the initial number of fibers

and their presence in the lowest density value was reduced by ten times.

28

(a)

(b)

Figure 3.7: High density fibers segmentation

(a) The Histograms illustrate the process of the high density fibers extraction

(b) To the left an axial slice of the tract density image in JET color map: the red represents a

high density value and the purple a low density value. To the right the fibers before

(white) and after (blue) the high density extraction process.

29

3.3 Calculating a risk map

Weighing

Calculating the risk map is a necessary step for planning trajectories. The risk map set a risk

factor due to the influence of the four major structures that were mentioned in the previous

section (structures segmentation): ventricles, blood vessels, fMRI and high density fibers. As the

presence of the segmented structures grows, the risk value grows in each pixel. In a clinical

aspect it means that alerted areas which involve a higher number of significance factors would be

set with a higher risk value.

The following equation represents the risk value for each voxel :

α =

While is the normalized value due to the following decreasing order:

Structure

Blood vessels

fMRI

Fibers

Ventricles

Weighing and ranking functional areas

We offer the neurosurgeon a novel tool in which functional activities could be ranked due to

their significance. The user interface includes of four main functionalities: Motor, Sensory,

vision and speech while the neurosurgeon can choose the order of significance. The moral

human concept behind it is a neurosurgeon can rank functionality due to the patient’s skills and

specialty.

30

Figure 3.8: Risk map

The graphical user interface (left) and the risk map image (right). The edges marks represent the

sensory (blue) and the motor (orange) functional areas. That GUI enables to drag and drop

functional areas due to their significance.

The risk map as a vector image

In order to avoid a case in which the trajectories are intersected with blood vessels or ventricles,

a vector image has been selected as the suitable representation.

In this case three components were added to each pixel: two Booleans represented the presence

of blood vessels or ventricles and one float value represented the risk factor as was calculated in

the risk map. This representation enables to retrieval data more efficiently and to decrease the

number of input images in the next stage of calculating trajectories.

31

3.4 Calculating and visualizing safe trajectories

The role of this section is calculating a required number of trajectories while each trajectory is

picked from a different region in the head surface. The trajectories and each entry point in the

head surface are then colored due to their risk value.

In the first stage some entry points were rejected due to a geometrical angle constraint: if the

angle between the trajectory and the surface’s normal is higher or lower than 30 – that entry

point is rejected.

The equation that describes the trajectory’s risk value is as follows:

With that calculated trajectory’s risk values each entry point gets its value due to the summarized

risk. The entries points with the minimal risk values for each corresponding trajectory from each

region were colored due to their risk values after normalization. The result is color coded

trajectories and a color coded head surface (Fig 3.9, 3.10).

Note: as mentioned in the calculating risk map section, each pixel in the risk map image

represents three components: the calculated risk value and two Boolean parameters which

indicate whether or not that pixel is crossed by blood vessels or ventricles. Thus, in that

summarized process, an additional check was manipulated: testing the presence of blood vessels

and ventricles in order to reject trajectories that may cause to neuronal damages.

The selected trajectories were picked in each region due to the following equation:

In order to keep fluency, the selected trajectories from each region were sorted due to their risk

value and were visualized in increasing order to the user (Fig. 3.11).

32

Figure 3.9: Color coded trajectories and head surface

The colored trajectories are the automatic trajectories while the white trajectories are the doctors’

trajectories and will be discussed in greater detail in the experimental result section.

Figure 3.10: Segmented structures and color coded head surface

The structures segmentation: red- blood vessels, dark purple- motor fMRI, blue- sensory fMRI,

brown- vision fMRI, yellow- vision fibers and light blue- motor fibers.

The color coded head surface: green- low risk, yellow- medium risk and red- high risk.

3.5 Graphical user interface

In order to be the most compatible to the neurosurgeon time limitation, we minimize the number

of input images to one (instead of three, thanks to the risk map vector image) and a head surface

for the optional entry points. The interaction with the user requires one click to determine the

target point and the required number of trajectories. The output is the color coded head surface

and trajectories in three orthogonal views and 3D models.

33

Fig. 3.11: Planning Trajectories - user interface

A data manager (left), a coronal slice (second to the left), a 3D model of the color coded risk

map (third to the left) and the required controllers: the definition of the target point and the

number of trajectories (right).

Software platform

The software platform was based on the Medical Imaging Interaction Toolkit (MITK) which is a

free open-source software system for development of interactive medical image processing

software. MITK combines the Insight Toolkit (ITK) and the Visualization Toolkit (VTK) with an

application framework. As a toolkit, MITK offers those features that are relevant for the

development of interactive medical imaging software covered neither by ITK nor VTK.

Mitk platform includes of three main components: modules, plugins and apps and the correlation

between them is presented in Fig. 3.12. The modules component includes of algorithms which

may call to ITK/VTK libraries. The plugins component consists of two types of files: cpp for

managing the inputs that Qt gets such as nodes of images, values etc. For each of those

components CMake defines the source files for the compilation process. The startup project

which is the executable file of the program is generated by the solution file that CMake

generates. As Mitk is an open source program, the correlation with developers is made by Git, a

distribution revision control and code management system. Thus, in each software update – Git

has to be involved in order to keep the web updated.

Our executable version consists of four main plugins: the first for calculating trajectory, the

second for calculating risk map, with the weighing procedure, the third for structures

segmentation (separately for each structure) and the fourth for experimental results.

34

Figure 3.12: MITK platform diagram

35

Chapter 4

Experimental results

This chapter describes the results of the algorithm evaluation. Section 4.1 describes the data sets

used in the experiments. Section 4.2 describes the methodology and section 4.3 presents the

results numeric and visually.

4.1 Datasets description

We obtained MRI datasets of five patients with different types of neuronal diagnosis (Table 1).

Of these, three targets are located at the left hemisphere of the brain and two in the other

hemisphere. Each of the five datasets consists of two types of MRI image: T1 with gadolinium,

T1 without gadolinium and two types of data matrices that were acquired from diffusion MRI,

for visualizing fibers bundle, and functional MRI, for visualizing functional areas. The MRI

images were acquired at the Hadassa Ein Karem hospital. Slice resolution is 512 x 512 x 256.

The voxel size in the datasets is 0.47 x 0.47 x 1 . The scans show targets in various internal

locations: frontal, parietal, temporal and occipital lobes. For each patient different functional

areas were observed such as the motor, sensory, vision and speech. Table 1 shows the medical

data regarding each patient. The diagnosis was determined by the doctor, the various locations of

the target were mapped into regions due to an internal deviation (Figure 4.1) which separated the

head to hemispheres and lobes. The alignment between functional data and T1 scan was

automatically registered by rigid registration with minimum three points in curved structures.

4.2 Methodology

To evaluate our planning trajectories method to the doctors’ method four surgeons, one expert

and three trainers, defined their entry points selection, one trajectory for each patient while the

target point was defined once for each patient by the surgeon. The following protocol describes x

stages in the experiment. In the first stage each dataset, including the T1 with gadolinium MRI

image and the structures segmentation, were presented to the surgeon. The surgeon defined his

best choice of entry point due to his knowledge and experience while watching the structures

segmentation. With the predefined target point, a surface trajectory was generated and the

surgeon could observe his trajectory selection by the three orthogonal slices in probe eye view

mode. Each of the trajectories per every patient was compared to the automatic trajectories that

our software calculated. The minimum distance was calculated from each trajectory to the

structures segmentation as well as the trajectory’s risk value from the risk map image and the

trajectory length.

36

Figure 4.1: Internal deviation of the brain, coronal view

Patient Diagnosis Hemisphere Region fMRI Fibers Alignment

EN LLG Left 4,6 Vision

Speech

Vision

SLF

Yes

SO GLIOBLASTOMA Left 1,3 Vision

Speech

Vision

SLF

Yes

HD OLIGO Right 6 Motor

Sensory

Motor

Vision

Sensory

No

AO KAVANORMA Right 3,4 Sensory

Motor

Vision

Wernicke

Motor

Sensory

Vision

SLF

No

ST LLG left 3,4 Motor

Speech

Motor No

Table 4.1: Datasets of patients

*fMRI of speech is divided into three components: Broka, Wernicke and peripheral areas.

4.3 Results

Structures segmentation

The structures segmentation is an essential stage before the calculating trajectories stage. Thus,

for each patient four main structures were segmented: blood vessels, ventricles, fMRI and fibers

bundles of different functional activities (Table 4.1). The structure segmentation algorithms are

manipulated and can be easily used by the plugins and scripts that we developed. Table 4.2

shows a time analysis of that process in order to achieve one dataset.

37

Structure Procedure Tool Time Images

Blood

vessels

Vesselness algorithm and

corrections

Mitk- our developed module

Itk snap

2-3 h 1

Ventricles

Region growing Mitk- our developed plug in 2 m 1

Head

surface

Extraction algorithm

Triangulation, smoothing and

shading

ROI surface extraction

Mitk- our developed plug in

Para view and VTK

Para view

3m

5m

4m

1

High

density

Fibers

Turning points into lines

Selecting areas due to ROI

Tract density image

Thresholding

Matlab

Mitk

Mitk- our developed module

Matlab

1m

2m

1m

1m

2-4

fMRI

Turning points into a binary

image

Selecting areas due to ROI

Matlab

Mitk/Itk snap

1m

1m

2-4

Total Structures segmentation Mitk, Itk and Vtk, Matlab and

Paraview

0.5-2h(bv)+28-

42m

Table 4.2: Segmentation Process of a patient – time analysis

Minimum distance

In order to examine both of the methods, the automatic method and the doctors method, the

minimum distance of each trajectory to each structure was calculated. In order to keep the results

coherent, we calculated dummy values such as the average structure which is the average of all

the structures (blood vessels, ventricles fibers bundle and fMRI) and the average doctor which is

the average of all the doctors (three trainees and an expert). The trainees are Dr1, Dr2 and Dr3

and the senior doctor is the expert (Table 4.3).

38

Color coded head surface and trajectories

To assist the neurosurgeon in selecting the safest insertion trajectory, candidate points in the

predefined entry point area on the outer head surface are color coded due to their risk value and

displayed on a 3D image. Each candidate entry point is colored with respect to the risk value that

was computed on its trajectory. Fig. 4.2 shows the color coded head surface for each patient and

the manual (white cylinders, black head) and automatic (color coded) trajectories. The head

surface was subdivided into small polygons such that the resolution would be higher (thus the

sharps colored polygons on the surface). To the left the region of interest (the optional entries

points) with the MRI T1gad image, to the right a view from inside the brain (the target point).

Structure Dr 1 Dr 2 Dr 3 Expert Doctors Auto

BV 1.7 (2.6) 1.9 (1.0) 1.9 (1.2) 2.7 (1.0) 2.0 (0.5) 3.3 (2.4)

ventricles 19.3 (13.4) 19.5 (13.4) 18.5 (12.9) 17.9 (17.6) 18.8 (0.7) 15.9 (12.7)

fMRI 20.4 (10.6) 19.1 (11.3) 18.3 (11.3) 28.0 (9.1) 21.4 (4.5) 22.7 (9.7)

Fibers 12.4 (7.6) 12.0 (7.5) 10.2 (7.1) 12.9 (1.8) 11.9 (1.2) 12.1 (5.8)

Average 13.4 (8.5) 13.1 (5.4) 12.2 (8.1) 15.4 (7.4) 13.5 (1.3) 13.5 (7.7)

Table 4.3: Minimum distance – Each structure for all the patients

Patient Dr 1 Dr 2 Dr 3 Expert Doctors Auto

HD 14.6 (11.0) 12.8 (11.3) 11.7 (10.2)

13.0 (1.4) 12.5 (7.0)

ST 14.3 (9.9) 14.7 (10.8) 13.3 (9.8) 13.3 (8.5) 14.1 (0.7) 11.9 (5.7)

AO 7.1 (6.3) 8.4 (6.2) 7.7 (6.7)

7.7 (0.6) 11.1 (11.0)

EN 14.0 (16.2) 10.4 (15.5) 10.1 (15.1) 14.3 (17.2) 11.5 (2.2) 13.8 (15.8)

SO 17.1 (15.7) 19.4 (14.5) 18.3 (14.7) 18.6 (14.5) 18.3 (0.9) 18.1 (15.1)

ALL 13.4 (11.8) 13.1 (11.7) 12.2 (11.3) 15.4 (13.4) 12.9 (1.2) 13.5 (10.9)

Table 4.4: Minimum distance – Each patient for all the structures

Distance Dr 1 Dr 2 Dr 3 Expert Doctors Auto DIFF

min dist 0.5 (0.9) 1.7 (1.3) 1.2 (1.0) 1.5 (1.2) 1.2 (1.1) 2.8 (2.5) +1.6

max dist 27.3 (9.5) 26.7 (8.4) 25.5 (8.5) 31.6 (9.2) 27.8 (8.9) 27.4 (8.2) -0.4

avg dist 13.4 (3.8) 13.1 (4.2) 12.2 (4.0) 15.4 (2.8) 13.5 (3.7) 13.5 (2.8) 0.0

Table 4.5: Minimum distance – distances of all patients and all structures

39

Patient SO: GLIOBLASTOMA, left hemisphere

Patient AO: KAVANORMA, right hemisphere

40

Patient ST: LLG, left hemisphere

Sagittal view

Coronal view

41

Figure 4.2: Color coded head surface and trajectories for each patient

The white trajectories are the doctors’ trajectories and the colored are the automatic trajectories.

Red represents high risk, yellow medium risk and green low risk.

Sagittal view

Patient HD: OLIGO, right hemisphere

Patient EN: LLG, left hemisphere

42

Chapter 5

Conclusions

This chapter concludes the thesis. Section 5.1 summarizes the thesis’s background and goals.

Section 5.2 discusses contributes. Section 5.3 discusses the method’s limitation. Section 5.4

discusses possible improvements and future work.

5.1 Summary

Many image-guided keyhole neurosurgery procedures require the precise targeting of tumors and

anatomical structures with a surgical tool inside the brain based on pre-operative CT/MRI

images. A misplacement of the surgical tool from the planned trajectory may result severe

neurological complications. Consequently, it is desired to select a trajectory that is located at a

safe distance from critical structures such as blood vessels, ventricles and functional areas such

as motor, sensory, vision and speech which are represented by fibers bundle and fMRI.

We have developed software, a fast automatic method for planning safe trajectories based on the

conventional 3D orthogonal planes. A graphical user interface (GUI) for the method was

developed and used as an experimental system by trainers and experienced neurosurgeons.

5.2 Discussion

The large amount of numbers which represent the minimum distance of each trajectory from

each structure, for each patient to each doctor, can be very confusing. In this section we will

explain our method results in order to conclude the properties of our method.

Complexity: The routine method requires the neurosurgeon to summarize in their minds the risk

value of any trajectory. Thus, a large amount of data such as a multiple number of functional

MRI and fibers bundles and internal targets might cause this difficult rise. Thus in these

representing cases the automatic method might be in advantage relatively to the routine method:

43

1. Patient AO, in addition to his internal target (Figure 4.1), a large amount of functional

dataset such as fMRI and fibers bundles (Table 4.1), cause to trajectories selection with

the lowest standard deviation relatively to all the other patients. In this case, out method

has an advantage of both a minimum distance to each structure and a standard deviation

relatively to the routine method (Table 4.4).

2. The target of patient EN is located in the most internal relatively to all the other patients.

Thus we consider this case as the most complicated in term of trajectory selection. As the

results show (Table 4.4), this case has the most significance improvement in terms of

both the minimum distance and the standard deviation. Our notion is that this standard

deviation was achieved thanks to the unlimited ability of complex calculations of the

automatic method relatively to the human thinking limitation. Thus, even if the number

of neurosurgeons would be greater the same results would be achieved.

Trajectory risk value

In order to estimate the quality of each trajectory, the risk value was calculated from the risk

map. Each doctor’s trajectory calculated for each patient including the average trajectory of the

automatic method (Fig. 5.2). The tendency of those risk values shows that as the years of

experience grow, the trajectory quality is improved. In addition, the automatic trajectory risk

value achieved the lowest risk value for each one of the patient. An additional observation is the

improvement of the same doctor as the number of tries grows. Thus we can conclude that the

presentation of the structures segmentation in the experiment has a significance impact for the

planning trajectory process. Table 5.1 shows the normalized risk values for each patient which

were achieved by the trainees, the expert and the automatic method. The normalization was made

by the minimal and maximal risk values that calculated for each patient. As the last row presents,

the automatic method achieves a risk value of 25% of the expert’s risk value and 19% of the

average trainee’s risk values.

44

Patient Average trainee Expert Auto

HD 0.22

0.06

ST 0.20 0.24 0.04

AO 0.31

0.06

EN 0.14 0.10 0.02

SO 0.17 0.15 0.02

Average patient 0.21 0.16 0.04

Table 5.1: Normalized trajectories risk values

Figure 5.2: Comparison of trajectories risk values - manual versus automatic methods

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

HD ST AO EN SO

Trajectories risk values per patient

Average trainee

Expert

Auto

0.00

0.05

0.10

0.15

0.20

0.25

Average trainee Expert Auto

Trajectories risk values - all patients

45

5.3 Contributions

This thesis makes five main contributions to the state of the art:

1. Planning with functional MRI and DTI data: In addition to ventricles and blood

vessels segmentation, we added segmentations of fibers bundle and fMRI for planning

trajectories. The segmentation of fibers bundle was built as poly lines which can yield (a)

a density image as we calculated and extracted the high density fibers (b) a further

experiment such as tracking by the fiber’s direction and finding specific locations in the

brain such as in DBS procedure (described in section 1.2).

2. User interface for segmenting and planning: We designed and implemented GUI with

most of the basic image processing tools that a neurosurgeon need such as three

orthogonal axes, clicking ability for target definition, plugins for structures segmentation

and planning trajectories including calculations for examining trajectories.

3. Weighing and ranking functional activities: We developed a ranking and weighing

ability for risk map generation. This method can be considered as the first step towards a

personalize medicine by weighing major and minor functional activities due to the patient

information such as occupation and will be discussed later (the significance of the speech

ability of a politician patient should be higher than other functional activity).

4. Observing patients with internal targets: As we discussed earlier, the most significant

results were achieved for patients with internal targets due to the human thinking

limitations. Our automatic method has no calculations limitations, thus for any amount of

dataset for each patient in any location, our method plan the safest trajectories from each

region on the head.

5. Color coded head surface and trajectories: The automatic method preserves the risk

values of any optional trajectory in the region of interest. Thus the visualization of color

coded trajectories and head surface can be presented to the neurosurgeon as an indicator

of high a low risks areas due to the weighted risk map.

6. Simplicity and speed of the planning process: The simplicity of the user interface and

the output visualization makes our approach unique among others for planning

trajectories. While other methods require additional input or a lot of manual work to the

surgeon, our approach requires just one click for a target definition.

46

5.4 Limitations

The following limitations were studied during the software development:

1. ROI of the output trajectories

Only the surgeon has the overall knowledge of the medical decisions that he has to make

when planning trajectories. The software, on the other hand, selects trajectories due the

region of interest that was inserted. Thus, long trajectories were selected as safest

trajectories even if they don’t acceptable to the surgeon. An idea of how to improve this

topic was to remove trajectories with a longer length of 40% the shortest length. But the

specific number is not sure yet and have to be considered in a further research.

2. Sulcus segmentation

The sulcus considered as a structure in the brain which we can’t cross by a trajectory (like

the blood vessels). Because of this structure wasn’t segmented, we manually checked that

the safest trajectory don’t pass thorough this structure. If we had achieved this

segmentation, we could have expended the vector risk map with this Boolean data.

5.5 Future work

Personalized medicine

Personalized medicine is a young but rapidly advancing field of healthcare that is

informed by each person's unique clinical, genomic, and environmental information. We

suggest a ranking process that optimizes the trajectory selection correspondingly. With

the GUI of the ranking functional activities that we offer, the neurosurgeon can prefer

activities due the patient preferences. Thus, in a further experiment an additional data

about each patient can be collected such as occupation.

Sulci segmentation

As we discussed in previous section, the Sulci wasn’t segmented. A vector risk map

which contains data about Sulci presence in each pixel would reject trajectories at the

early stage.

47

Chapter 6

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