Image databases in medical applications

The number of medical images acquired yearly in hospitals increases all the time. These imaging data contain lots of information on the characteristics of anatomical structures and on their variations. This information can be utilized in numerous medical applications. In deformable model-based segmentation and registration methods, the information in the image databases can be used to give a priori information on the shape of the object studied and the gray-level values in the image, and on their variations. On the other hand, by studying the variations of the object of interest in different populations, the effects of, for example, aging, gender, and diseases on anatomical structures can be detected. In the work described in this Thesis, methods that utilize image databases in medical applications were studied. Methods were developed and compared for deformable model-based segmentation and registration. Model selection procedure, mean models, and combination of classifiers were studied for the construction of a good a priori model. Statistical and probabilistic shape models were generated to constrain the deformations in segmentation and registration so that only the shapes typical to the object studied were accepted. In the shape analysis of the striatum, both volume and local shape changes were studied. The effects of aging and gender, and also the asymmetries were examined. The results proved that the segmentation and registration accuracy of deformable model-based methods can be improved by utilizing the information in image databases. The databases used were relatively small. Therefore, the statistical and probabilistic methods were not able to model all the population-specific variation. On the other hand, the simpler methods, the model selection procedure, mean models, and combination of classifiers, gave good results also with the small image databases. Two main applications were the reconstruction of 3-D geometry from incomplete data and the segmentation of heart ventricles and atria from short- and long-axis magnetic resonance images. In both applications, the methods studied provided promising results. The shape analysis of the striatum showed that the volume of the striatum decreases in aging. Also, the shape of the striatum changes locally. Asymmetries in the shape were found, too, but any gender-related local shape differences were not found.reviewe

A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Schizophrenia is associated with widespread alterations in subcortical brain structure.While analytic methods have enabled more detailed morphometric characterization,findings are often equivocal. In this meta-analysis, we employed the harmonizedENIGMA shape analysis protocols to collaboratively investigate subcortical brainstructure shape differences between individuals with schizophrenia and healthy con-trol participants. The study analyzed data from 2,833 individuals with schizophreniaand 3,929 healthy control participants contributed by 21 worldwide research groupsparticipating in the ENIGMA Schizophrenia Working Group. Harmonized shape analy-sis protocols were applied to each site's data independently for bilateral hippocam-pus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained fromT1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens,and thalamus in individuals with schizophrenia compared with control participants,more-convex-than-concave shape differences in the putamen and pallidum, and bothconcave and convex shape differences in the caudate. Patterns of exaggerated asym-metry were observed across the hippocampus, amygdala, and thalamus in individualswith schizophrenia compared to control participants, while diminished asymmetryencompassed ventral striatum and ventral and dorsal thalamus. Our analyses also rev-ealed that higher chlorpromazine dose equivalents and increased positive symptomlevels were associated with patterns of contiguous convex shape differences acrossmultiple subcortical structures. Findings from our shape meta-analysis suggest thatcommon neurobiological mechanisms may contribute to gray matter reduction acrossmultiple subcortical regions, thus enhancing our understanding of the nature of net-work disorganization in schizophrenia

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

Quantitative analysis of group-specific brain tissue probability map for schizophrenic patients

We developed group-specific tissue probability map (TPM) for gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) on the common spatial coordinates of an averaged brain atlas derived from normal controls (NC) and from schizophrenic patients (SZ). To identify differences in group-specific TPMs, we used quantitative evaluation methods based on differences in probabilistic distribution as a global criterion, and the mean probability and the similarity index (SI) by lobe as regional criteria. The SZ group showed more spatial variation with a lower mean probability than NC subjects. And, for the right temporal and left parietal lobes, the SI between each group was lower than the other lobes. It can be said that there were significant differences in spatial distribution between controls and schizophrenic patients at those areas. In case of female group, although group differences in the volumes of GM and WM were not significant, global difference in the probabilistic distribution of GM was more prominent and the SI was lower and its descent rate was greater in all lobes, compared with the male group. If these morphological differences caused by disease or group-specific features were not considered in TPM, the accuracy and certainty of specific group studies would be greatly reduced. Therefore, suitable TPM is required as a common framework for functional neuroimaging studies and an a priori knowledge of tissue classification

Hippocampus Shape Analysis and Late-Life Depression

Major depression in the elderly is associated with brain structural changes and vascular lesions. Changes in the subcortical regions of the limbic system have also been noted. Studies examining hippocampus volumetric differences in depression have shown variable results, possibly due to any volume differences being secondary to local shape changes rather than differences in the overall volume. Shape analysis offers the potential to detect such changes. The present study applied spherical harmonic (SPHARM) shape analysis to the left and right hippocampi of 61 elderly subjects with major depression and 43 non-depressed elderly subjects. Statistical models controlling for age, sex, and total cerebral volume showed a significant reduction in depressed compared with control subjects in the left hippocampus (F1,103 = 5.26; p = 0.0240) but not right hippocampus volume (F1,103 = 0.41; p = 0.5213). Shape analysis showed significant differences in the mid-body of the left (but not the right) hippocampus between depressed and controls. When the depressed group was dichotomized into those whose depression was remitted at time of imaging and those who were unremitted, the shape comparison showed remitted subjects to be indistinguishable from controls (both sides) while the unremitted subjects differed in the midbody and the lateral side near the head. Hippocampal volume showed no difference between controls and remitted subjects but nonremitted subjects had significantly smaller left hippocampal volumes with no significant group differences in the right hippocampus. These findings may provide support to other reports of neurogenic effects of antidepressants and their relation to successful treatment for depressive symptoms

Automated hippocampal location and extraction

The hippocampus is a complex brain structure that has been studied extensively and is subject to abnormal structural change in various neuropsychiatric disorders. The highest definition in vivo method of visualizing the anatomy of this structure is structural Magnetic Resonance Imaging (MRI). Gross structure can be assessed by the naked eye inspection of MRI scans but measurement is required to compare scans from individuals within normal ranges, and to assess change over time in individuals. The gold standard of such measurement is manual tracing of the boundaries of the hippocampus on scans. This is known as a Region Of Interest (ROI) approach. ROI is laborious and there are difficulties with test-retest and inter-rater reliability. These difficulties are primarily due to uncertainty in designation of the hippocampus boundary. An improved, less labour intensive and more reliable method is clearly desirable. This thesis describes a fully automated hybrid methodology that is able to first locate and then extract hippocampal volumes from 3D 1.5T MRI T1 brain scans automatically. The hybrid algorithm uses brain atlas mappings and fuzzy inference to locate hippocampal areas and create initial hippocampal boundaries. This initial location is used to seed a deformable manifold algorithm. Rule based deformations are then applied to refine the estimate of the hippocampus locations. Finally, the hippocampus boundaries are corrected through an inference process that assures adherence to an expected hippocampus volume. The ICC values of this methodology when compared to the manual segmentation of the same hippocampi result in a 0.73 for the left and 0.81 for the right hippocampi. These values both fall within the range of reliability testing according to the manual ‘gold standard’ technique. Thus, this thesis describes the development and validation of a genuinely automated approach to hippocampal volume extraction of potential utility in studies of a range of neuropsychiatric disorders and could eventually find clinical applications

Influence of APOE Genotype on Hippocampal Atrophy over Time - An N=1925 Surface-Based ADNI Study

abstract: The apolipoprotein E (APOE) e4 genotype is a powerful risk factor for late-onset Alzheimer’s disease (AD). In the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, we previously reported significant baseline structural differences in APOE e4 carriers relative to non-carriers, involving the left hippocampus more than the right—a difference more pronounced in e4 homozygotes than heterozygotes. We now examine the longitudinal effects of APOE genotype on hippocampal morphometry at 6-, 12- and 24-months, in the ADNI cohort. We employed a new automated surface registration system based on conformal geometry and tensor-based morphometry. Among different hippocampal surfaces, we computed high-order correspondences, using a novel inverse-consistent surface-based fluid registration method and multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance. At each time point, using Hotelling’s T[superscript 2] test, we found significant morphological deformation in APOE e4 carriers relative to non-carriers in the full cohort as well as in the non-demented (pooled MCI and control) subjects at each follow-up interval. In the complete ADNI cohort, we found greater atrophy of the left hippocampus than the right, and this asymmetry was more pronounced in e4 homozygotes than heterozygotes. These findings, combined with our earlier investigations, demonstrate an e4 dose effect on accelerated hippocampal atrophy, and support the enrichment of prevention trial cohorts with e4 carriers.The article is published at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.015290