176 research outputs found

    BrainPrint: A discriminative characterization of brain morphology

    Get PDF
    We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace–Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.National Cancer Institute (U.S.) (1K25-CA181632-01)Athinoula A. Martinos Center for Biomedical Imaging (P41-RR014075)Athinoula A. Martinos Center for Biomedical Imaging (P41-EB015896)National Alliance for Medical Image Computing (U.S.) (U54-EB005149)Neuroimaging Analysis Center (U.S.) (P41-EB015902)National Center for Research Resources (U.S.) (U24 RR021382)National Institute of Biomedical Imaging and Bioengineering (U.S.) (5P41EB015896-15)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)National Institute on Aging (AG022381)National Institute on Aging (5R01AG008122-22)National Institute on Aging (AG018344)National Institute on Aging (AG018386)National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01)National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534)National Institutes of Health (U.S.) ((5U01-MH093765

    BrainPrint: A discriminative characterization of brain morphology

    Get PDF
    We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace–Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.National Cancer Institute (U.S.) (1K25-CA181632-01)Athinoula A. Martinos Center for Biomedical Imaging (P41-RR014075)Athinoula A. Martinos Center for Biomedical Imaging (P41-EB015896)National Alliance for Medical Image Computing (U.S.) (U54-EB005149)Neuroimaging Analysis Center (U.S.) (P41-EB015902)National Center for Research Resources (U.S.) (U24 RR021382)National Institute of Biomedical Imaging and Bioengineering (U.S.) (5P41EB015896-15)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)National Institute on Aging (AG022381)National Institute on Aging (5R01AG008122-22)National Institute on Aging (AG018344)National Institute on Aging (AG018386)National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01)National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534)National Institutes of Health (U.S.) ((5U01-MH093765

    3D shape matching and registration : a probabilistic perspective

    Get PDF
    Dense correspondence is a key area in computer vision and medical image analysis. It has applications in registration and shape analysis. In this thesis, we develop a technique to recover dense correspondences between the surfaces of neuroanatomical objects over heterogeneous populations of individuals. We recover dense correspondences based on 3D shape matching. In this thesis, the 3D shape matching problem is formulated under the framework of Markov Random Fields (MRFs). We represent the surfaces of neuroanatomical objects as genus zero voxel-based meshes. The surface meshes are projected into a Markov random field space. The projection carries both geometric and topological information in terms of Gaussian curvature and mesh neighbourhood from the original space to the random field space. Gaussian curvature is projected to the nodes of the MRF, and the mesh neighbourhood structure is projected to the edges. 3D shape matching between two surface meshes is then performed by solving an energy function minimisation problem formulated with MRFs. The outcome of the 3D shape matching is dense point-to-point correspondences. However, the minimisation of the energy function is NP hard. In this thesis, we use belief propagation to perform the probabilistic inference for 3D shape matching. A sparse update loopy belief propagation algorithm adapted to the 3D shape matching is proposed to obtain an approximate global solution for the 3D shape matching problem. The sparse update loopy belief propagation algorithm demonstrates significant efficiency gain compared to standard belief propagation. The computational complexity and convergence property analysis for the sparse update loopy belief propagation algorithm are also conducted in the thesis. We also investigate randomised algorithms to minimise the energy function. In order to enhance the shape matching rate and increase the inlier support set, we propose a novel clamping technique. The clamping technique is realized by combining the loopy belief propagation message updating rule with the feedback from 3D rigid body registration. By using this clamping technique, the correct shape matching rate is increased significantly. Finally, we investigate 3D shape registration techniques based on the 3D shape matching result. Based on the point-to-point dense correspondences obtained from the 3D shape matching, a three-point based transformation estimation technique is combined with the RANdom SAmple Consensus (RANSAC) algorithm to obtain the inlier support set. The global registration approach is purely dependent on point-wise correspondences between two meshed surfaces. It has the advantage that the need for orientation initialisation is eliminated and that all shapes of spherical topology. The comparison of our MRF based 3D registration approach with a state-of-the-art registration algorithm, the first order ellipsoid template, is conducted in the experiments. These show dense correspondence for pairs of hippocampi from two different data sets, each of around 20 60+ year old healthy individuals

    Doctor of Philosophy

    Get PDF
    dissertationAn important aspect of medical research is the understanding of anatomy and its relation to function in the human body. For instance, identifying changes in the brain associated with cognitive decline helps in understanding the process of aging and age-related neurological disorders. The field of computational anatomy provides a rich mathematical setting for statistical analysis of complex geometrical structures seen in 3D medical images. At its core, computational anatomy is based on the representation of anatomical shape and its variability as elements of nonflat manifold of diffeomorphisms with an associated Riemannian structure. Although such manifolds effectively represent natural biological variability, intrinsic methods of statistical analysis within these spaces remain deficient at large. This dissertation contributes two critical missing pieces for statistics in diffeomorphisms: (1) multivariate regression models for cross-sectional study of shapes, and (2) generalization of classical Euclidean, mixed-effects models to manifolds for longitudinal studies. These models are based on the principle that statistics on manifold-valued information must respect the intrinsic geometry of that space. The multivariate regression methods provide statistical descriptors of the relationships of anatomy with clinical indicators. The novel theory of hierarchical geodesic models (HGMs) is developed as a natural generalization of hierarchical linear models (HLMs) to describe longitudinal data on curved manifolds. Using a hierarchy of geodesics, the HGMs address the challenge of modeling the shape-data with unbalanced designs typically arising as a result of follow-up medical studies. More generally, this research establishes a mathematical foundation to study dynamics of changes in anatomy and the associated clinical progression with time. This dissertation also provides efficient algorithms that utilize state-of-the-art high performance computing architectures to solve models on large-scale, longitudinal imaging data. These manifold-based methods are applied to predictive modeling of neurological disorders such as Alzheimer's disease. Overall, this dissertation enables clinicians and researchers to better utilize the structural information available in medical images

    Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

    Get PDF
    Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries

    ApoE4 effects on the structural covariance brain networks topology in Mild Cognitive Impairment

    Get PDF
    The Apolipoprotein E isoform E4 (ApoE4) is consistently associated with an elevated risk of developing late-onset Alzheimer's Disease (AD). However, little is known about his potential genetic modulation on the structural covariance brain networks during prodromal stages like Mild Cognitive Impairment (MCI). The covariance phenomenon is based on the observation that regions correlating in morphometric descriptors are often part of the same brain system. In a first study, I assessed the ApoE4-related changes on the brain network topology in 256 MCI patients, using the regional cortical thickness to define the covariance network. The cross-sectional sample selected from the ADNI database was subdivided into ApoE4-positive (Carriers) and negative (non-Carriers). At the group-level, the results showed a significant decrease in characteristic path length, clustering index, local efficiency, global connectivity, modularity, and increased global efficiency for Carriers compared to non-Carriers. Overall, I found that ApoE4 in MCI shaped the topological organization of cortical thickness covariance networks. In the second project, I investigated the impact of ApoE4 on the single-subject gray matter networks in a sample of 200 MCI from the ADNI database. The patients were classified based on clinical outcome (stable MCI versus converters to AD) and ApoE4 status (Carriers versus non-Carriers). The effects of ApoE4 and disease progression on the network measures at baseline and rate of change were explored. The topological network attributes were correlated with AD biomarkers. The main findings showed that gray matter network topology is affected independently by ApoE4 and the disease progression (to AD) in late-MCI. The network measures alterations showed a more random organization in Carriers compared to non-Carriers. Finally, as additional research, I investigated whether a network-based approach combined with the graph theory is able to detect cerebrovascular reactivity (CVR) changes in MCI. Our findings suggest that this experimental approach is more sensitive to identifying subtle cerebrovascular alterations than the classical experimental designs. This study paves the way for a future investigation on the ApoE4-cerebrovascular interaction effects on the brain networks during AD progression. In summary, my thesis results provide evidence of the value of the structural covariance brain network measures to capture subtle neurodegenerative changes associated with ApoE4 in MCI. Together with other biomarkers, these variables may help predict disease progression, providing additional reliable intermediate phenotypes

    Predicting Alzheimer's disease by segmenting and classifying 3D-brain MRI images using clustering technique and SVM classifiers.

    Get PDF
    Alzheimer's disease (AD) is the most common form of dementia affecting seniors age 65 and over. When AD is suspected, the diagnosis is usually confirmed with behavioural assessments and cognitive tests, often followed by a brain scan. Advanced medical imaging and pattern recognition techniques are good tools to create a learning database in the first step and to predict the class label of incoming data in order to assess the development of the disease, i.e., the conversion from prodromal stages (mild cognitive impairment) to Alzheimer's disease. Advanced medical imaging such as the volumetric MRI can detect changes in the size of brain regions due to the loss of the brain tissues. Measuring regions that atrophy during the progress of Alzheimer's disease can help neurologists in detecting and staging the disease. In this thesis, we want to diagnose the Alzheimer’s disease from MRI images. We segment brain MRI images to extract the brain chambers. Then, features are extracted from the segmented area. Finally, a classifier is trained to differentiate between normal and AD brain tissues. We discuss an automatic scheme that reads volumetric MRI, extracts the middle slices of the brain region, performs 2-dimensional (volume slices) and volumetric segmentation methods in order to segment gray matter, white matter and cerebrospinal fluid (CSF), generates a feature vector that characterizes this region, creates a database that contains the generated data, and finally classifies the images based on the extracted features. For our results, we have used the MRI data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database1. We assessed the performance of the classifiers by using results from the clinical tests.Master of Science (M.Sc.) in Computational Science

    Computer aided diagnosis in temporal lobe epilepsy and Alzheimer's dementia

    Get PDF
    Computer aided diagnosis within neuroimaging must rely on advanced image processing techniques to detect and quantify subtle signal changes that may be surrogate indicators of disease state. This thesis proposes two such novel methodologies that are both based on large volumes of interest, are data driven, and use cross-sectional scans: appearance-based classification (ABC) and voxel-based classification (VBC).The concept of appearance in ABC represents the union of intensity and shape information extracted from magnetic resonance images (MRI). The classification method relies on a linear modeling of appearance features via principal components analysis, and comparison of the distribution of projection coordinates for the populations under study within a reference multidimensional appearance eigenspace. Classification is achieved using forward, stepwise linear discriminant analyses, in multiple cross-validated trials. In this work, the ABC methodology is shown to accurately lateralize the seizure focus in temporal lobe epilepsy (TLE), differentiate normal aging individuals from patients with either Alzheimer's dementia (AD) or Mild Cognitive Impairment (MCI), and finally predict the progression of MCI patients to AD. These applications demonstrated that the ABC technique is robust to different signal changes due to two distinct pathologies, to low resolution data and motion artifacts, and to possible differences inherent to multi-site acquisition.The VBC technique relies on voxel-based morphometry to identify regions of grey and white matter concentration differences between co-registered cohorts of individuals, and then on linear modeling of variables extracted from these regions. Classification is achieved using linear discriminant analyses within a multivariate space composed of voxel-based morphometry measures related to grey and white matter concentration, along with clinical variables of interest. VBC is shown to increase the accuracy of prediction of one-year clinical status from three to four out of five TLE patients having undergone selective amygdalo-hippocampectomy. These two techniques are shown to have the necessary potential to solve current problems in neurological research, assist clinical physicians with their decision-making process and influence positively patient management

    Classification of dementias based on brain radiomics features

    Get PDF
    Dissertação de mestrado integrado em Engenharia InformáticaNeurodegenerative diseases impair the functioning of the brain and are characterized by alterations in the morphology of specific brain regions. Some of the main disorders include Alzheimer's, Parkinson's, and Huntington's diseases, and the number of cases increases exponentially since ageing is one of the main risk factors. Trying to identify the areas in which this type of disease appears is something that can have a very positive impact in this area of Medicine and can guarantee a more appropriate treatment or allow the improvement of the quality of life of patients. With the current technological advances, computer tools are capable of performing a structural or functional analysis of neuroimaging data from Magnetic Resonance Images(MRI). Therefore, Medical Informatics uses these techniques to create and manage medical neuroimaging data to improve the diagnosis and management of these patients. MRI is the image type used in the analysis of the brain area and points to a promising and reliable diagnostic tool since it allows high-quality images in various planes or strategies and MRI methods are fundamental diagnostic tools in clinical practice, allowing the diagnosis of pathologic processes such as stroke or brain tumours. However, structural MRI has limitations for the diagnosis of neurodegenerative disorders since it mainly identifies atrophy of brain regions. Currently, there is increased interest in informatics applications capable of monitoring and quantifying human brain imaging alterations, with potential for neurodegenerative disorders diagnosis and monitoring. One of these applications is Radiomics, which corresponds to a methodolog ythat allows the extraction of features from images of a given region of the brain. Specific quantitative metrics from MRI are acquired by this tool, and they correspond to a set of features, including texture, shape, among others. To standardize Radiomics application, specific libraries have been proposed to be used by the bioinformatics and biomedical communities, such as PyRadiomics, which corresponds to an open source Python package for extracting Radiomics of MRIs. Therefore, this dissertation was developed based on magnetic resonance images and the study of Deep Learning (DL) techniques to assist researchers and neuroradiologists in the diagnosis and prediction of neurodegenerative disease development. Two different main tasks were made: first, a segmentation, using FreeSurfer, of different regions of the brain and then, a model was build from radiomic features extracted from each part of the brain and interpreted for knowledge extraction.As doenças neurodegenerativas estão associadas ao funcionamento do cérebro e caracterizam-se pelo facto de serem altamente incapacitantes. São exemplos destas, as doenças de Alzheimer, Parkinson e Huntington, e o seu número de casos tem vindo a aumentar exponencialmente, uma vez que o envelhecimento é um dos principais factores de risco. Tentar identificar quais são as regiões cerebrais que permitem predizer o seu aparecimento e desenvolvimento é algo que, sendo possível, terá um impacto muito positivo nesta área da Medicina e poderá garantir um tratamento mais adequado, ou simplesmente melhorar a qualidade de vida dos pacientes. Com os avanços tecnológicos atuais, foram desenvolvidas ferramentas informáticas que são capazes de efetuar uma análise estrutural ou funcional de Ressonâncias Magnéticas (MRI), sendo essas ferramentas usadas para promover a melhoria e o conhecimento clínico. Deste modo, as constantes evoluções científicas têm realçado o papel da Informática Médica na neuroimagem para criar e gerenciar dados médicos, melhorando o diagnóstico destes pacientes. A MRI é o tipo de imagem utilizada na análise de regiões cerebrais e aponta para uma ferramenta de diagnóstico promissora e fiável, uma vez que permite obter imagens de alta qualidade em vários planos, permitindo assim, o diagnóstico de processos patológicos, tais como acidentes vasculares ou tumores cerebrais. Atualmente, existem inúmeras aplicações informáticas capazes de efetuar análises estruturais e funcionais do cérebro humano, pois é este o principal órgão afetado pelas doenças neurodegenerativas. Uma dessas aplicações é o Radiomics, que permite fazer a extração de features de imagens do cérebro. A biblioteca a utilizar será PyRadiomics, que corresponde a um package open source em Python para a extração de features Radiomics de imagens médicas. As features correspondem a características da imagem. Assim sendo, a presente dissertação foi desenvolvida com base em imagens de ressonância magnética e no estudo das técnicas de Deep Learning para investigar e auxiliar os médicos neurorradiologistas a diagnosticar e a prever o desenvolvimento de doenças neurodegenerativas. Foram feitas duas principais tarefas: primeiro, uma segmentação, utilizando o software FreeSurfer, de diferentes regiões do cérebro e, de seguida, foi construído um modelo a partir das features radiómicas extraídas de cada parte do cérebro que foi interpretado
    corecore