95 research outputs found

    Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms

    Get PDF
    We propose a method to learn a distribution of shape trajectories from longitudinal data, i.e. the collection of individual objects repeatedly observed at multiple time-points. The method allows to compute an average spatiotemporal trajectory of shape changes at the group level, and the individual variations of this trajectory both in terms of geometry and time dynamics. First, we formulate a non-linear mixed-effects statistical model as the combination of a generic statistical model for manifold-valued longitudinal data, a deformation model defining shape trajectories via the action of a finite-dimensional set of diffeomorphisms with a manifold structure, and an efficient numerical scheme to compute parallel transport on this manifold. Second, we introduce a MCMC-SAEM algorithm with a specific approach to shape sampling, an adaptive scheme for proposal variances, and a log-likelihood tempering strategy to estimate our model. Third, we validate our algorithm on 2D simulated data, and then estimate a scenario of alteration of the shape of the hippocampus 3D brain structure during the course of Alzheimer's disease. The method shows for instance that hippocampal atrophy progresses more quickly in female subjects, and occurs earlier in APOE4 mutation carriers. We finally illustrate the potential of our method for classifying pathological trajectories versus normal ageing

    Learning low-dimensional representations of shape data sets with diffeomorphic autoencoders

    Get PDF
    Auteur collectif : Alzheimer’s Disease Neuroimaging InitiativeInternational audienceContemporary deformation-based morphometry offers parametric classes of diffeomorphisms that can be searched to compute the optimal transformation that warps a shape into another, thus defining a similarity metric for shape objects. Extending such classes to capture the geometrical variability in always more varied statistical situations represents an active research topic. This quest for genericity however leads to computationally-intensive estimation problems. Instead, we propose in this work to learn the best-adapted class of diffeomorphisms along with its parametrization, for a shape data set of interest. Optimization is carried out with an auto-encoding variational inference approach, offering in turn a coherent model-estimator pair that we name diffeomorphic auto-encoder. The main contributions are: (i) an original network-based method to construct diffeomorphisms, (ii) a current-splatting layer that allows neural network architectures to process meshes, (iii) illustrations on simulated and real data sets that show differences in the learned statistical distributions of shapes when compared to a standard approach

    Shape analysis of the human brain.

    Get PDF
    Autism is a complex developmental disability that has dramatically increased in prevalence, having a decisive impact on the health and behavior of children. Methods used to detect and recommend therapies have been much debated in the medical community because of the subjective nature of diagnosing autism. In order to provide an alternative method for understanding autism, the current work has developed a 3-dimensional state-of-the-art shape based analysis of the human brain to aid in creating more accurate diagnostic assessments and guided risk analyses for individuals with neurological conditions, such as autism. Methods: The aim of this work was to assess whether the shape of the human brain can be used as a reliable source of information for determining whether an individual will be diagnosed with autism. The study was conducted using multi-center databases of magnetic resonance images of the human brain. The subjects in the databases were analyzed using a series of algorithms consisting of bias correction, skull stripping, multi-label brain segmentation, 3-dimensional mesh construction, spherical harmonic decomposition, registration, and classification. The software algorithms were developed as an original contribution of this dissertation in collaboration with the BioImaging Laboratory at the University of Louisville Speed School of Engineering. The classification of each subject was used to construct diagnoses and therapeutic risk assessments for each patient. Results: A reliable metric for making neurological diagnoses and constructing therapeutic risk assessment for individuals has been identified. The metric was explored in populations of individuals having autism spectrum disorders, dyslexia, Alzheimers disease, and lung cancer. Conclusion: Currently, the clinical applicability and benefits of the proposed software approach are being discussed by the broader community of doctors, therapists, and parents for use in improving current methods by which autism spectrum disorders are diagnosed and understood

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

    Full text link
    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

    Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms

    Get PDF
    International audienceWe propose a method to learn a distribution of shape tra-jectories from longitudinal data, i.e. the collection of individual objects repeatedly observed at multiple time-points. The method allows to compute an average spatiotemporal trajectory of shape changes at the group level, and the individual variations of this trajectory both in terms of geometry and time dynamics. First, we formulate a non-linear mixed-effects statistical model as the combination of a generic statistical model for manifold-valued longitudinal data, a deformation model defining shape trajectories via the action of a finite-dimensional set of diffeomorphisms with a man-ifold structure, and an efficient numerical scheme to compute parallel transport on this manifold. Second, we introduce a MCMC-SAEM algorithm with a specific approach to shape sampling, an adaptive scheme for proposal variances , and a log-likelihood tempering strategy to estimate our model. Third, we validate our algorithm on 2D simulated data, and then estimate a scenario of alteration of the shape of the hippocampus 3D brain structure during the course of Alzheimer's disease. The method shows for instance that hippocampal atrophy progresses more quickly in female subjects, and occurs earlier in APOE4 mutation carriers. We finally illustrate the potential of our method for classifying pathological trajectories versus normal ageing

    Three Dimensional Nonlinear Statistical Modeling Framework for Morphological Analysis

    Get PDF
    This dissertation describes a novel three-dimensional (3D) morphometric analysis framework for building statistical shape models and identifying shape differences between populations. This research generalizes the use of anatomical atlases on more complex anatomy as in case of irregular, flat bones, and bones with deformity and irregular bone growth. The foundations for this framework are: 1) Anatomical atlases which allow the creation of homologues anatomical models across populations; 2) Statistical representation for output models in a compact form to capture both local and global shape variation across populations; 3) Shape Analysis using automated 3D landmarking and surface matching. The proposed framework has various applications in clinical, forensic and physical anthropology fields. Extensive research has been published in peer-reviewed image processing, forensic anthropology, physical anthropology, biomedical engineering, and clinical orthopedics conferences and journals. The forthcoming discussion of existing methods for morphometric analysis, including manual and semi-automatic methods, addresses the need for automation of morphometric analysis and statistical atlases. Explanations of these existing methods for the construction of statistical shape models, including benefits and limitations of each method, provide evidence of the necessity for such a novel algorithm. A novel approach was taken to achieve accurate point correspondence in case of irregular and deformed anatomy. This was achieved using a scale space approach to detect prominent scale invariant features. These features were then matched and registered using a novel multi-scale method, utilizing both coordinate data as well as shape descriptors, followed by an overall surface deformation using a new constrained free-form deformation. Applications of output statistical atlases are discussed, including forensic applications for the skull sexing, as well as physical anthropology applications, such as asymmetry in clavicles. Clinical applications in pelvis reconstruction and studying of lumbar kinematics and studying thickness of bone and soft tissue are also discussed

    Combining shape and intensity dxa-based statistical approaches for osteoporotic HIP fracture risk assessment

    Get PDF
    5noAiming to improve osteoporotic hip fracture risk detection, factors other than the largely adopted Bone Mineral Density (BMD) have been investigated as potential risk predictors. In particular Hip Structural Analysis (HSA)-derived parameters accounting for femur geometry, extracted from Dual-energy X-ray Absorptiometry (DXA) images, have been largely considered as geometric risk factors. However, HSA-derived parameters represent discrete and cross-correlated quantities, unable to describe proximal femur geometry as a whole and tightly related to BMD. Focusing on a post-menopausal cohort (N = 28), in this study statistical models of bone shape and BMD distribution have been developed to investigate their possible role in fracture risk. Due to unavailable retrospective patient-specific fracture risk information, here a surrogate fracture risk based on 3D computer simulations has been employed for the statistical framework construction. When considered separately, BMD distribution performed better than shape in explaining the surrogate fracture risk variability for the analysed cohort. However, the combination of BMD and femur shape quantities in a unique statistical model yielded better results. In detail, the first shape-intensity combined mode identified using a Partial Least Square (PLS) algorithm was able to explain 70% of the surrogate fracture risk variability, thus suggesting that a more effective patients stratification can be obtained applying a shape-intensity combination approach, compared to T-score. The findings of this study strongly advocate future research on the role of a combined shape-BMD statistical framework in fracture risk determination.partially_openembargoed_20211027Aldieri A.; Terzini M.; Audenino A.L.; Bignardi C.; Morbiducci U.Aldieri, A.; Terzini, M.; Audenino, A. L.; Bignardi, C.; Morbiducci, U

    Blood flow dynamics at the pulmonary artery bifurcation

    Get PDF
    Knowledge of physiologic hemodynamics is a fundamental requirement to establish pathological findings. However, little is known about the normal flow fields in the pulmonary arteries, especially for children. The purpose of this study is to characterize flow patterns in the pulmonary artery bifurcation of healthy pediatric subjects using direct numerical simulations. A realistic geometry is obtained via statistical shape modeling, by averaging five subject-specific digital models extracted from cardiovascular magnetic resonance datasets of healthy volunteers. Boundary conditions are assigned to mimic physiological conditions at rest, corresponding to a peak Reynolds number equal to 3400 and a Womersley number equal to 15. Results show that the normal bifurcation is highly hemodynamically efficient, as measured by an energy dissipation index. The curvature of the pulmonary arteries is sufficiently small to prevent flow separation along the inner walls, and no signs of a turbulent-like state are found. In line with previous imaging studies, a helical structure protruding into the right pulmonary artery is detected, and its formation mechanism is elucidated in the paper. These findings might help to identify abnormal flow features in patients with altered anatomic and physiologic states, particularly those with repaired congenital heart disease.Peer ReviewedPostprint (published version

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

    Get PDF
    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
    corecore