Master of Science Thesis in Electrical Engineering Department of Electrical Engineering, Linköping University, 2017 Evaluation of 3D MRI image registration methods Magnus Ivarsson Master of Science Thesis in Electrical Engineering Evaluation of 3D MRI image registration methods Magnus Ivarsson LiTH-ISY-EX--17/5037--SE Supervisor: Andreas Robinson cvl, Linköping University Thobias Romu amra Magnus Borga amra Examiner: Maria Magnusson cvl, Linköping University Computer Vision Laboratory Department of Electrical Engineering Linköping University SE-581 83 Linköping, Sweden Copyright © 2017 Magnus Ivarsson Abstract Image registration is the process of geometrically deforming a template image into a reference image. This technique is important and widely used within the field of medical IT. The purpose could be to detect image variations, pathological development or in the company AMRA’s case, to quantify fat tissue in various parts of the human body. From an MRI (Magnetic Resonance Imaging) scan, a water and fat tissue image is obtained. Currently, AMRA is using the Morphon algorithm to register and seg- ment the water image in order to quantify fat and muscle tissue. During the first part of this master thesis, two alternative registration methods were evaluated. The first algorithm was Free Form Deformation which is a non-linear parametric based method. The second algorithm was a non-parametric optical flow based method known as the Demon algorithm. During the second part of the thesis, the Demon algorithm was used to evaluate the effect of using the fat images for registrations. iii Acknowledgments I would like to thank my supervisor Andreas Robinson and examinor Maria Mag- nusson at CVL for your great support during my master thesis. I would also like to thank my supervisors Thobias Romu and Magnus Borga for giving me the opportunity to complete my master thesis at AMRA and for your continuous support throughout the project. Linköping, Januari 2017 Magnus Ivarsson v Contents Notation ix 1 Introduction 1 1.1 Background . 1 1.1.1 Aim . 4 1.2 Problem Formulation . 4 1.3 Limitations . 4 2 Theory 7 2.1 Image registration - an overview . 7 2.1.1 Parametric methods . 8 2.1.2 Non-parametric methods . 10 2.1.3 Landmark based registration . 12 2.2 Distance measures . 13 2.2.1 SSD . 14 2.2.2 Normalized Cross Correlation . 15 2.2.3 Normalized Gradient Field . 15 2.2.4 Mutual Information . 16 2.3 Image registration as an optimization problem . 16 2.3.1 Regularization methods . 16 2.4 Methods . 17 2.4.1 Morphon . 18 2.4.2 Demon . 20 2.4.3 Free Form Deformation . 21 2.5 Evaluation . 22 2.5.1 Body Composition Measurements . 22 2.5.2 Segmentation metrics . 24 3 Method 29 3.1 Algorithm comparison . 29 3.1.1 Morphon . 32 3.1.2 Free form deformation . 32 3.1.3 Demon . 33 vii viii Contents 3.2 Further investigation . 34 3.3 Region definitions . 35 3.4 Dataset . 36 3.4.1 Region masks . 37 3.5 Deformation and segmentation . 39 3.5.1 Prototype deformations . 40 3.5.2 Prototype selection . 40 3.5.3 Probability field . 41 3.5.4 Segmentation . 42 3.5.5 Disjoint regions . 42 3.6 Evaluation . 42 3.6.1 Body composition measurements . 42 3.6.2 Weighted segmentation . 44 3.6.3 Statistics . 45 4 Results 47 4.1 Algorithm comparison . 47 4.1.1 Visceral adipose tissue . 48 4.1.2 Abdominal subcutaneous adipose tissue . 50 4.1.3 Muscles . 52 4.2 Further investigation . 54 4.2.1 Visceral adipose tissue . 55 4.2.2 Abdominal subcutaneous adipose tissue . 57 4.2.3 Muscles . 59 5 Discussion 63 5.1 Results . 63 5.1.1 Algorithm comparison . 64 5.1.2 Further investigation . 64 5.2 Method . 66 5.2.1 Flawed and biased ground truth . 66 5.2.2 Incomplete evaluation of registrations . 66 5.2.3 Unequal tuning of algorithms . 67 5.2.4 Limited set of prototypes . 67 5.2.5 Training and evaluation set . 67 5.3 Future work . 68 5.3.1 Refine ground truth . 68 5.3.2 Evaluating registrations differently . 68 5.3.3 Detecting and handling outliers . 68 5.3.4 Extend prototype bank . 68 6 Conclusions 71 Bibliography 73 Notation Notation Meaning IT Template image IR Reference image Ω Domain where an image I is defined u Displacement field δu Incremental displacement field c Certainty field I Image gradient rD Distance/Similarity measure T Transformation (unless something else is specified) R Regularization x Bold lowercase character indicate vector A Bold uppercase character indicate matrix I;I Scalar product between two images h · ui Divergence of a displacement field r Convolution T ∗ I Transformation applied to an image ◦ Abbreviation Meaning MRI Magnetic resonance imaging VAT Visceral adipose tissue ASAT Abdominal subcutaneous adipose tissue SAT Subcutaneous adipose tissue LULB Left upper leg back (muscle) LULF Left upper leg front (muscle) RULB Right upper leg back (muscle) RULF Right upper leg front (muscle) IP In phase (water + fat image) ix 1 Introduction This chapter is intended to give the reader the necessary background information and motivation of why image registration of MR images is useful and the aim of the master thesis. The problem formulation and the limitations of this thesis are also described. 1.1 Background With an aging population, an efficient and improved health care is essential to handle the demands we put on our health care system. Medical imaging and image registration of medical images will play an important role in the future. In today’s society, health and metabolic status for individuals are often obtained by indirect measurements such as BMI and waist circumference. However, alternate methods exist: AMRA is a company that specializes in precise body composition measurements that can improve and customize treatments for individuals with high metabolic risk. This is done by generating a water and fat image from an MRI scan followed by a quantification of fat and muscle tissue in different regions of the body. This quantification is possible because of two reasons: First, the water image indicate which voxels that contain muscle tissue while the fat image indicate which voxels contain fat tissue. Second, the images are normalized so that the voxel intensity correspond to the concentration of fat or muscle tissue within that particular voxel. An example of a water and fat image can be seen in figure 1.1 and images of segmented adipose and muscle tissue can be seen in figure 1.2. 1 2 1 Introduction Figure 1.1: The image to the left illustrates a water image, the image in the middle illustrates a fat image and the image to the right illustrates an IP image (water + fat) in the coronal plane. Note that the water signal is strong where the fat signal is weak and vice versa. Image source: AMRA. Figure 1.2: The images to the left illustrate the upper leg muscle regions. Blue pixels represent left upper leg back, yellow represents left upper leg front, magenta represents right upper leg back and cyan represents right upper leg front. In the image to the right, red pixels represent VAT and blue pixels represent ASAT. Image source: AMRA. 1.1 Background 3 Examples of measurements AMRA provide are • VAT - Visceral Adipose Tissue (intra-abdominal fat) • ASAT - Abdominal Subcutaneous Adipose Tissue (pincheable fat) • Thigh Muscle Volume • Lean Muscle Tissue Volume • Total Adipose Tissue • Additional Muscle Group Volumes The reason why the body is segmented into different regions is that it often mat- ters where fat tissue is located. For instance, research has shown that large vol- umes of visceral adipose tissue is connected to diabetes, liver disease, and cancer [15]. On the other hand, subcutaneous fat is much less likely to have adverse health effects [16]. Tools that are used today, like BMI and waist circumference, are well suited to determine health and metabolic status on a population basis but can sometimes be crude when used to determine the body composition of an individual. An image that illustrates the difference between BMI and VAT can be seen in figure 1.3. Figure 1.3: Example of six men with a BMI of 21 where their body composi- tion and metabolic risk have a high variation. The blue color represents ab- dominal subcutaneous adipose tissue and red color represents visceral adi- pose tissue. Image source: AMRA. 4 1 Introduction So far, the reasons why precise body composition measurements are necessary have been explained. However, the method by which the regions of interest are found, i.e. how to segment the image, has not yet been described. A simple but time-consuming way to segment an image is manual labelling of each voxel. An- other approach, which is more cost effective, is to use image registration methods to automatically segment an image with the aid of a computer. Note that the precision is not guaranteed to be better by manual segmentation compared to au- tomatic. Today, AMRA is using the Morphon algorithm in order to register and segment an MR image. 1.1.1 Aim The aim of the master thesis is to evaluate different registration algorithms and to determine whether the results given by the Morphon can be improved. The evaluation was carried out as explained in section 2.5.1 and 3.6. 1.2 Problem Formulation The master thesis has been divided into two parts. The first part was devoted to answer the following questions: • Is it possible to obtain better segmentation with the parametric method Free Form Deformation (explained in section 2.1.1) compared to the Morphon? • Is it possible to obtain better segmentation with the Demon algorithm (ex- plained in section 2.4.2) compared to the Morphon? In the second part, the Demon algorithm was used to answer the following ques- tions: • Is it possible to obtain better segmentation if the fat image is used as input to the registration algorithm? • Is it possible to obtain better segmentation if a combination of the fat and water image is used as input to the registration algorithm? 1.3 Limitations Because of the limited time frame of the master thesis, the methods chosen for evaluation are within the group of parametric and non-parametric methods.