Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.Comment: Paper under revie

Segmentation of brain MRI during early childhood

The objective of this thesis is the development of automatic methods to measure the changes in volume and growth of brain structures in prematurely born infants. Automatic tools for accurate tissue quantification from magnetic resonance images can provide means for understanding how the neurodevelopmental effects of the premature birth, such as cognitive, neurological or behavioural impairment, are related to underlying changes in brain anatomy. Understanding these changes forms a basis for development of suitable treatments to improve the outcomes of premature birth. In this thesis we focus on the segmentation of brain structures from magnetic resonance images during early childhood. Most of the current brain segmentation techniques have been focused on the segmentation of adult or neonatal brains. As a result of rapid development, the brain anatomy during early childhood differs from anatomy of both adult and neonatal brains and therefore requires adaptations of available techniques to produce good results. To address the issue of anatomical differences of the brain during early childhood compared to other age-groups, population-specific deformable and probabilistic atlases are introduced. A method for generation of population-specific prior information in form of a probabilistic atlas is proposed and used to enhance existing segmentation algorithms. The evaluation of registration-based and intensity-based approaches shows the techniques to be complementary in the quality of automatic segmentation in different parts of the brain. We propose a novel robust segmentation method combining the advantages of both approaches. The method is based on multiple label propagation using B-spline non-rigid registration followed by EM segmentation. Intensity inhomogeneity is a shading artefact resulting from the acquisition process, which significantly affects modern high resolution MR data acquired at higher magnetic field strengths. A novel template based method focused on correcting the intensity inhomogeneity in data acquired at higher magnetic field strengths is therefore proposed. The proposed segmentation method combined with proposed intensity inhomogeneity correction method offers a robust tool for quantification of volumes and growth of brain structures during early childhood. The tool have been applied to 67 T1-weigted images of subject at one and two years of age

Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy

Automated Morphometric Characterization of the Cerebral Cortex for the Developing and Ageing Brain

Morphometric characterisation of the cerebral cortex can provide information about patterns of brain development and ageing and may be relevant for diagnosis and estimation of the progression of diseases such as Alzheimer's, Huntington's, and schizophrenia. Therefore, understanding and describing the differences between populations in terms of structural volume, shape and thickness is of critical importance. Methodologically, due to data quality, presence of noise, PV effects, limited resolution and pathological variability, the automated, robust and time-consistent estimation of morphometric features is still an unsolved problem. This thesis focuses on the development of tools for robust cross-sectional and longitudinal morphometric characterisation of the human cerebral cortex. It describes techniques for tissue segmentation, structural and morphometric characterisation, cross-sectional and longitudinally cortical thickness estimation from serial MRI images in both adults and neonates. Two new probabilistic brain tissue segmentation techniques are introduced in order to accurately and robustly segment the brain of elderly and neonatal subjects, even in the presence of marked pathology. Two other algorithms based on the concept of multi-atlas segmentation propagation and fusion are also introduced in order to parcelate the brain into its multiple composing structures with the highest possible segmentation accuracy. Finally, we explore the use of the Khalimsky cubic complex framework for the extraction of topologically correct thickness measurements from probabilistic segmentations without explicit parametrisation of the edge. A longitudinal extension of this method is also proposed. The work presented in this thesis has been extensively validated on elderly and neonatal data from several scanners, sequences and protocols. The proposed algorithms have also been successfully applied to breast and heart MRI, neck and colon CT and also to small animal imaging. All the algorithms presented in this thesis are available as part of the open-source package NiftySeg

Analysis of Sub-Cortical Morphology in Benign Epilepsy with Centrotemporal Spikes

RÉSUMÉ Au Canada, l’épilepsie affecte environ 5 à 8 enfants par 3222 âgés de 2 à 37 ans dans la population globale. Quinze à 47 % de ces enfants ont une épilepsie bénigne avec des pointes centrotemporelles (BECTS), ce qui fait de BECTS le syndrome épileptique focal de l’enfant bénin le plus fréquent. Initialement, BECTS était considéré comme bénin parmi les autres épilepsies car il était généralement rapporté que les capacités cognitives ont été préservées ou ramenées à la normale pendant la rémission. Cependant, certaines études ont trouvé des déficits cognitifs et comportementaux, qui peuvent bien persister même après la rémission. Compte tenu des différences neurocognitives chez les enfants atteints de BECTS et de témoins normaux, la question est de savoir si des variations morphométriques subtiles dans les structures cérébrales sont également présentes chez ces patients et si elles expliquent des variations dans les performence cognitifs. En fait, malgré les preuves accumulées d’une étiologie neurodéveloppementale dans le BECTS, peu est connu sur les altérations structurelles sous-jacentes. À cet égard, la proposition de méthodes avancées en neuroimagerie permettrait d’évaluer quantitativement les variations de la morphologie cérébrale associées à ce trouble neurologique. En outre, l’étude du développement morphologique du cerveau et sa relation avec la cognition peut aider à élucider la base neuroanatomique des déficits cognitifs. Le but de cette thèse est donc de fournir un ensemble d’outils pour analyser les variations morphologiques sous-corticales subtiles provoquées par différentes maladies, telles que l’épilepsie bénigne avec des pointes centrotemporelles. La méthodologie adoptée dans cette thèse a conduit à trois objectifs de recherche spécifiques. La première étape vise à développer un nouveau cadre automatisé pour segmenter les structures sous-corticales sur les images à resonance magnètique (IRM). La deuxième étape vise à concevoir une nouvelle approche basée sur la correspondance spectrale pour capturer précisément la variabilité de forme chez les sujets épileptiques. La troisième étape conduit à une analyse de la relation entre les changements morphologiques du cerveau et les indices cognitifs. La première contribution vise plus spécifiquement la segmentation automatique des structures sous-corticales dans un processus de co-recalage et de co-segmentation multi-atlas. Contrairement aux approches standards de segmentation multi-atlas, la méthode proposée obtient la segmentation finale en utilisant un recalage en fonction de la population, tandis que les connaissances à prior basés sur les réseaux neuronaux par convolution (CNNs) sont incorporées dans la formulation d’énergie en tant que représentation d’image discriminative. Ainsi, cette méthode exploite des représentations apprises plus sophistiquées pour conduire le processus de co-recalage. De plus, étant donné un ensemble de volumes cibles, la méthode proposée calcule les probabilités de segmentation individuellement, puis segmente tous les volumes simultanément. Par conséquent, le fardeau de fournir un sous-ensemble de vérité connue approprié pour effectuer la segmentation multi-atlas est évité. Des résultats prometteurs démontrent le potentiel de notre méthode sur deux ensembles de données, contenant des annotations de structures sous-corticales. L’importance des estimations fiables des annotations est également mise en évidence, ce qui motive l’utilisation de réseaux neuronaux profonds pour remplacer les annotations de vérité connue en co-recalage avec une perte de performance minimale. La deuxième contribution vise à saisir la variabilité de forme entre deux populations de surfaces en utilisant une analyse morphologique multijoints. La méthode proposée exploite la représentation spectrale pour établir des correspondances de surface, puisque l’appariement est plus simple dans le domaine spectral plutôt que dans l’espace euclidien conventionnel. Le cadre proposé intègre la concordance spectrale à courbure moyenne dans un plateforme d’analyse de formes sous-corticales multijoints. L’analyse expérimentale sur des données cliniques a montré que les différences de groupe extraites étaient similaires avec les résultats dans d’autres études cliniques, tandis que les sorties d’analyse de forme ont été créées d’une manière à réduire le temps de calcul. Enfin, la troisième contribution établit l’association entre les altérations morphologiques souscorticales chez les enfants atteints d’épilepsie bénigne et les indices cognitifs. Cette étude permet de détecter les changements du putamen et du noyau caudé chez les enfants atteints de BECTS gauche, droit ou bilatéral. De plus, l ’association des différences volumétriques structurelles et des différences de forme avec la cognition a été étudiée. Les résultats confirment les altérations de la forme du putamen et du noyau caudé chez les enfants atteints de BECTS. De plus, nos résultats suggèrent que la variation de la forme sous-corticale affecte les fonctions cognitives. Cette étude démontre que les altérations de la forme et leur relation avec la cognition dépendent du côté de la focalisation de l’épilepsie. Ce projet nous a permis d’étudier si de nouvelles méthodes permettraient de traiter automatiquement les informations de neuro-imagerie chez les enfants atteints de BECTS et de détecter des variations morphologiques subtiles dans leurs structures sous-corticales. De plus, les résultats obtenus dans le cadre de cette thèse nous ont permis de conclure qu’il existe une association entre les variations morphologiques et la cognition par rapport au côté de la focalisation de la crise épileptique.----------ABSTRACT In Canada, epilepsy affects approximately 5 to 8 children per 3222 aged from 2 to 37 years in the overall population. Fifteen to 47% of these children have benign epilepsy with centrotemporal spikes (BECTS), making BECTS the most common benign childhood focal epileptic syndrome. Initially, BECTS was considered as benign among other epilepsies since it was generally reported that cognitive abilities were preserved or brought back to normal during remission. However, some studies have found cognitive and behavioral deficits, which may well persist even after remission. Given neurocognitive differences among children with BECTS and normal controls, the question is whether subtle morphometric variations in brain structures are also present in these patients, and whether they explain variations in cognitive indices. In fact, despite the accumulating evidence of a neurodevelopmental etiology in BECTS, little is known about underlying structural alterations. In this respect, proposing advanced neuroimaging methods will allow for quantitative assessment of variations in brain morphology associated with this neurological disorder. In addition, studying the brain morphological development and its relationship with cognition may help elucidate the neuroanatomical basis of cognitive deficits. Therefore, the focus of this thesis is to provide a set of tools for analyzing the subtle sub-cortical morphological alterations in different diseases, such as benign epilepsy with centrotemporal spikes. The methodology adopted in this thesis led to addressing three specific research objectives. The first step develops a new automated framework for segmenting subcortical structures on MR images. The second step designs a new approach based on spectral correspondence to precisely capture shape variability in epileptic individuals. The third step finds the association between brain morphological changes and cognitive indices. The first contribution aims more specifically at automatic segmentation of sub-cortical structures in a groupwise multi-atlas coregistration and cosegmentation process. Contrary to the standard multi-atlas segmentation approaches, the proposed method obtains the final segmentation using a population-wise registration, while Convolutional Neural Network (CNN)- based priors are incorporated in the energy formulation as a discriminative image representation. Thus, this method exploits more sophisticated learned representations to drive the coregistration process. Furthermore, given a set of target volumes the developed method computes the segmentation probabilities individually, and then segments all the volumes simultaneously. Therefore, the burden of providing an appropriate ground truth subset to perform multi-atlas segmentation is removed. Promising results demonstrate the potential of our method on two different datasets, containing annotations of sub-cortical structures. The importance of reliable label estimations is also highlighted, motivating the use of deep neural nets to replace ground truth annotations in coregistration with minimal loss in performance. The second contribution intends to capture shape variability between two population of surfaces using groupwise morphological analysis. The proposed method exploits spectral representation for establishing surface correspondences, since matching is simpler in the spectral domain rather than in the conventional Euclidean space. The designed framework integrates mean curvature-based spectral matching in to a groupwise subcortical shape analysis pipeline. Experimental analysis on real clinical dataset showed that the extracted group differences were in parallel with the findings in other clinical studies, while the shape analysis outputs were created in a computational efficient manner. Finally, the third contribution establishes the association between sub-cortical morphological alterations in children with benign epilepsy and cognitive indices. This study detects putamen and caudate changes in children with left, right, or bilateral BECTS to age and gender matched healthy individuals. In addition, the association of structural volumetric and shape differences with cognition is investigated. The findings confirm putamen and caudate shape alterations in children with BECTS. Also, our results suggest that variation in sub-cortical shape affects cognitive functions. More importantly, this study demonstrates that shape alterations and their relation with cognition depend on the side of epilepsy focus. This project enabled us to investigate whether new methods would allow to automatically process neuroimaging information from children afflicted with BECTS and detect subtle morphological variations in their sub-cortical structures. In addition, the results obtained in this thesis allowed us to conclude the existence of the association between morphological variations and cognition with respect to the side of seizure focus

Visual Exploration And Information Analytics Of High-Dimensional Medical Images

Data visualization has transformed how we analyze increasingly large and complex data sets. Advanced visual tools logically represent data in a way that communicates the most important information inherent within it and culminate the analysis with an insightful conclusion. Automated analysis disciplines - such as data mining, machine learning, and statistics - have traditionally been the most dominant fields for data analysis. It has been complemented with a near-ubiquitous adoption of specialized hardware and software environments that handle the storage, retrieval, and pre- and postprocessing of digital data. The addition of interactive visualization tools allows an active human participant in the model creation process. The advantage is a data-driven approach where the constraints and assumptions of the model can be explored and chosen based on human insight and confirmed on demand by the analytic system. This translates to a better understanding of data and a more effective knowledge discovery. This trend has become very popular across various domains, not limited to machine learning, simulation, computer vision, genetics, stock market, data mining, and geography. In this dissertation, we highlight the role of visualization within the context of medical image analysis in the field of neuroimaging. The analysis of brain images has uncovered amazing traits about its underlying dynamics. Multiple image modalities capture qualitatively different internal brain mechanisms and abstract it within the information space of that modality. Computational studies based on these modalities help correlate the high-level brain function measurements with abnormal human behavior. These functional maps are easily projected in the physical space through accurate 3-D brain reconstructions and visualized in excellent detail from different anatomical vantage points. Statistical models built for comparative analysis across subject groups test for significant variance within the features and localize abnormal behaviors contextualizing the high-level brain activity. Currently, the task of identifying the features is based on empirical evidence, and preparing data for testing is time-consuming. Correlations among features are usually ignored due to lack of insight. With a multitude of features available and with new emerging modalities appearing, the process of identifying the salient features and their interdependencies becomes more difficult to perceive. This limits the analysis only to certain discernible features, thus limiting human judgments regarding the most important process that governs the symptom and hinders prediction. These shortcomings can be addressed using an analytical system that leverages data-driven techniques for guiding the user toward discovering relevant hypotheses. The research contributions within this dissertation encompass multidisciplinary fields of study not limited to geometry processing, computer vision, and 3-D visualization. However, the principal achievement of this research is the design and development of an interactive system for multimodality integration of medical images. The research proceeds in various stages, which are important to reach the desired goal. The different stages are briefly described as follows: First, we develop a rigorous geometry computation framework for brain surface matching. The brain is a highly convoluted structure of closed topology. Surface parameterization explicitly captures the non-Euclidean geometry of the cortical surface and helps derive a more accurate registration of brain surfaces. We describe a technique based on conformal parameterization that creates a bijective mapping to the canonical domain, where surface operations can be performed with improved efficiency and feasibility. Subdividing the brain into a finite set of anatomical elements provides the structural basis for a categorical division of anatomical view points and a spatial context for statistical analysis. We present statistically significant results of our analysis into functional and morphological features for a variety of brain disorders. Second, we design and develop an intelligent and interactive system for visual analysis of brain disorders by utilizing the complete feature space across all modalities. Each subdivided anatomical unit is specialized by a vector of features that overlap within that element. The analytical framework provides the necessary interactivity for exploration of salient features and discovering relevant hypotheses. It provides visualization tools for confirming model results and an easy-to-use interface for manipulating parameters for feature selection and filtering. It provides coordinated display views for visualizing multiple features across multiple subject groups, visual representations for highlighting interdependencies and correlations between features, and an efficient data-management solution for maintaining provenance and issuing formal data queries to the back end