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

TOWARD SOLVING GROUPWISE MEDICAL IMAGE ANALYSIS PROBLEMS WITH DEEP LEARNING

Image regression, atlas building, and multi-atlas segmentation are three groupwise medical image analysis problems extended from image registration. These three problems are challenging because of the difficulty in establishing spatial correspondences and the associated high computational cost. Specifically, most previous methods are computationally costly as they are optimization-based approaches. Hence fast and accurate approaches are highly desirable. This dissertation addresses the following problems concerning the three groupwise medical im- age analysis problems: (1) fast and reliable geodesic regression for image time series; (2) joint atlas building and diffeomorphic registration learning; (3) efficient and accurate label fusion for multi-atlas segmentation; and (4) spatially localized probability calibration for semantic segmentation networks. Specifically, the contributions in this thesis are as follows: (1) A fast predictive simple geodesic regression approach is proposed to capture the frequently subtle deformation trends of longitudinal image data. (2) A new deep learning model that jointly builds an atlas and learns the diffeomorphic registrations in both the atlas-to-image and the image-to-atlas directions is developed. (3) A novel deep learning label fusion method (VoteNet) that locally identifies sets of trustworthy atlases is presented; and several ways to improve the performance under the VoteNet based multi-atlas segmentation framework are explored. (4) A learning-based local temperature scaling method that predicts a separate temperature scale for each pixel/voxel is designed. The resulting post-processing approach is accuracy preserving and is theoretically guaranteed to be effective.Doctor of Philosoph

CartiMorph: a framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient $\rho \in [0.82,0.97]$), surface area ($\rho \in [0.82,0.98]$) and volume ($\rho \in [0.89,0.98]$) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.Comment: To be published in Medical Image Analysi

Generative Models for Preprocessing of Hospital Brain Scans

I will in this thesis present novel computational methods for processing routine clinical brain scans. Such scans were originally acquired for qualitative assessment by trained radiologists, and present a number of difficulties for computational models, such as those within common neuroimaging analysis software. The overarching objective of this work is to enable efficient and fully automated analysis of large neuroimaging datasets, of the type currently present in many hospitals worldwide. The methods presented are based on probabilistic, generative models of the observed imaging data, and therefore rely on informative priors and realistic forward models. The first part of the thesis will present a model for image quality improvement, whose key component is a novel prior for multimodal datasets. I will demonstrate its effectiveness for super-resolving thick-sliced clinical MR scans and for denoising CT images and MR-based, multi-parametric mapping acquisitions. I will then show how the same prior can be used for within-subject, intermodal image registration, for more robustly registering large numbers of clinical scans. The second part of the thesis focusses on improved, automatic segmentation and spatial normalisation of routine clinical brain scans. I propose two extensions to a widely used segmentation technique. First, a method for this model to handle missing data, which allows me to predict entirely missing modalities from one, or a few, MR contrasts. Second, a principled way of combining the strengths of probabilistic, generative models with the unprecedented discriminative capability of deep learning. By introducing a convolutional neural network as a Markov random field prior, I can model nonlinear class interactions and learn these using backpropagation. I show that this model is robust to sequence and scanner variability. Finally, I show examples of fitting a population-level, generative model to various neuroimaging data, which can model, e.g., CT scans with haemorrhagic lesions

Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative inter-scan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD.Comment: Submitted to NeuroImage for revie

Deformable Medical Image Registration: A Survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this technical report, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this technical report is to provide an extensive account of registration techniques in a systematic manner.Le recalage déformable d'images est une des tâches les plus fondamentales dans l'imagerie médicale. Parmi ses applications les plus importantes, on compte: i) la fusion d' information provenant des différents types de modalités a n de faciliter le diagnostic et la planification du traitement; ii) les études longitudinales, oú des changements structurels ou anatomiques sont étudiées en fonction du temps; et iii) la modélisation de la variabilité anatomique normale d'une population et les atlas statistiques. Dans ce rapport de recherche, nous essayons de donner un aperçu des différentes méthodes du recalage déformables, en mettant l'accent sur les avancées les plus récentes du domaine. Nous avons particulièrement insisté sur les techniques appliquées aux images médicales. A n d'étudier les méthodes du recalage d'images, leurs composants principales sont d'abord identifiés puis étudiées de manière indépendante, les techniques les plus récentes étant classifiées en suivant un schéma logique déterminé. La contribution de ce rapport de recherche est de fournir un compte rendu détaillé des techniques de recalage d'une manière systématique

A Survey on Deep Learning in Medical Image Registration: New Technologies, Uncertainty, Evaluation Metrics, and Beyond

Over the past decade, deep learning technologies have greatly advanced the field of medical image registration. The initial developments, such as ResNet-based and U-Net-based networks, laid the groundwork for deep learning-driven image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, and uncertainty estimation. These advancements have not only enriched the field of deformable image registration but have also facilitated its application in a wide range of tasks, including atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D registration. In this paper, we present a comprehensive overview of the most recent advancements in deep learning-based image registration. We begin with a concise introduction to the core concepts of deep learning-based image registration. Then, we delve into innovative network architectures, loss functions specific to registration, and methods for estimating registration uncertainty. Additionally, this paper explores appropriate evaluation metrics for assessing the performance of deep learning models in registration tasks. Finally, we highlight the practical applications of these novel techniques in medical imaging and discuss the future prospects of deep learning-based image registration

Surface Registration for Pharyngeal Radiation Treatment Planning

Endoscopy is an in-body examination procedure that enables direct visualization of tumor spread on tissue surfaces. In the context of radiation treatment planning for throat cancer, there have been attempts to fuse this endoscopic information into the planning CT space for better tumor localization. One way to achieve this CT/Endoscope fusion is to first reconstruct a full 3D surface model from the endoscopic video and then register that surface into the CT space. These two steps both require an algorithm that can accurately register two or more surfaces. In this dissertation, I present a surface registration method I have developed, called Thin Shell Demons (TSD), for achieving the two goals mentioned above. There are two key aspects in TSD: geometry and mechanics. First, I develop a novel surface geometric feature descriptor based on multi-scale curvatures that can accurately capture local shape information. I show that the descriptor can be effectively used in TSD and other surface registration frameworks, such as spectral graph matching. Second, I adopt a physical thin shell model in TSD to produce realistic surface deformation in the registration process. I also extend this physical model for orthotropic thin shells and propose a probabilistic framework to learn orthotropic stiffness parameters from a group of known deformations. The anisotropic stiffness learning opens up a new perspective to shape analysis and allows more accurate surface deformation and registration in the TSD framework. Finally, I show that TSD can also be extended into a novel groupwise registration framework. The advantages of Thin Shell Demons allow us to build a complete 3D model of the throat, called an endoscopogram, from a group of single-frame-based reconstructions. It also allows us to register an endoscopogram to a CT segmentation surface, thereby allowing information transfer for treatment planning.Doctor of Philosoph