Unsupervised segmentation, clustering and groupwise registration of heterogeneous populations of brain MR images

Population analysis of brain morphology from magnetic resonance images contributes to the study and understanding of neurological diseases. Such analysis typically involves segmentation of a large set of images and comparisons of these segmentations between relevant subgroups of images (e.g., "normal" versus "diseased"). The images of each subgroup are usually selected in advance in a supervised way based on clinical knowledge. Their segmentations are typically guided by one or more available atlases, assumed to be suitable for the images at hand. We present a data-driven probabilistic framework that simultaneously performs atlas-guided segmentation of a heterogeneous set of brain MR images and clusters the images in homogeneous subgroups, while constructing separate probabilistic atlases for each cluster to guide the segmentation. The main benefits of integrating segmentation, clustering and atlas construction in a single framework are that: 1) our method can handle images of a heterogeneous group of subjects and automatically identifies homogeneous subgroups in an unsupervised way with minimal prior knowledge, 2) the subgroups are formed by automatical detection of the relevant morphological features based on the segmentation, 3) the atlases used by our method are constructed from the images themselves and optimally adapted for guiding the segmentation of each subgroup, and 4) the probabilistic atlases represent the morphological pattern that is specific for each subgroup and expose the groupwise differences between different subgroups. We demonstrate the feasibility of the proposed framework and evaluate its performance with respect to image segmentation, clustering and atlas construction on simulated and real data sets including the publicly available BrainWeb and ADNI data. It is shown that combined segmentation and atlas construction leads to improved segmentation accuracy. Furthermore, it is demonstrated that the clusters generated by our unsupervised framework largely coincide with the clinically determined subgroups in case of disease-specific differences in brain morphology and that the differences between the cluster-specific atlases are in agreement with the expected disease-specific patterns, indicating that our method is capable of detecting the different modes in a population. Our method can thus be seen as a comprehensive image-driven population analysis framework that can contribute to the detection of novel subgroups and distinctive image features, potentially leading to new insights in the brain development and disease.Ribbens A., Hermans J., Maes F., Vandermeulen D., Suetens P., ''Unsupervised segmentation, clustering and groupwise registration of heterogeneous populations of brain MR images'', IEEE transactions on medical imaging, vol. 33, no. 2, pp. 201-224, February 2014.status: publishe

Fernandez-Steel Skew Normal Mixture Model dengan Pendekatan Bayesian untuk Segmentasi Citra MRI Tumor Otak

Teknologi citra digital medis yang sering digunakan oleh pakar kesehatan untuk mendeteksi tumor otak pada pasien adalah Magnetic Resonance Imaging (MRI). Kesulitan dalam mengolah citra digital hasil MRI adalah memisahkan Region of Interest (ROI) dengan objek lain, sehingga perlu dilakukan segmentasi citra. Segmentasi citra dapat dilakukan dengan clustering. Metode clustering yang sering digunakan untuk segmentasi citra adalah Gaussian Mixture Model (GMM). Namun terda-pat kelemahan dari distribusi Gaussian, yaitu sifatnya yang berekor pendek dan simetris sehingga jika memerlukan model dengan ekor lebih panjang dapat didekati dengan banyak komponen distribusi Gaussian dalam membentuk mixture model. Hal tersebut mengakibatkan sifat parsimoni model kurang terjaga. Selain itu, pada kenyataannya histogram citra MRI tumor otak mengindikasikan adanya skewness. Oleh karena itu, alternatif dari permasalahan tersebut dengan menggunakan distribusi Neo-Normal. Pada penelitian ini dilakukan segmentasi citra MRI untuk mendeteksi lokasi tumor otak menggunakan Fernandez-Steel Skew Normal (FSSN) Mixture Model dengan Pendekatan Bayesian. Distribusi FSSN merupakan salah satu distribusi Neo-Normal yang membentuk distribusi Gaussian maupun Student's t yang dapat stabil dalam modus distribusinya. Pendekatan Bayesian digunakan karena pendekatan statistika klasik untuk estimasi parameter distribusi FSSN sangatlah rumit dan kompleks untuk diimplementasikan secara numerik. Hasil analisis menunjukkan bahwa distribusi FSSN lebih mampu merepresentasikan citra MRI tumor otak serta model yang didapatkan untuk segmentasi citra MRI tumor otak lebih parsimoni dibandingkan GMM. ==================================================================Medical digital imaging technology that is often used by health professionals to detect brain tumors in patients is Magnetic Resonance Imaging (MRI). Difficulty in processing digital image of the MRI is to identifying the separating Region of Interest (ROI) with other objects, so image segmentation is needed. Image segmentation can be done by clustering. Clustering method which is often used for image segmentation is the Gaussian Mixture Model (GMM). GMM has started to be abandoned because, in reality, the symmetric distribution approach is less able to explain the MRI data pattern. In addition, the use of symmetric distribution cannot compete for the model parsimony of an asymmetric distribution to model the long and heavy tail pattern of data. It needs more components in GMM. Therefore, an alternative to these problems is to employ the Neo-Normal distribution. Neo-Normal distribution is a relaxation of normality that is more adaptive to various characteristics of data than the Gaussian distribution. In this research, MRI image segmentation was performed to detect the location of brain tumors using Fernandez-Steel Skew Normal (FSSN) Mixture Model with Bayesian Approach. The FSSN distribution is one of the Neo-Normal distributions that can be skewed adaptively but still stable in its mode. Bayesian approach is used because the classical statistical approach for estimating FSSN distribution parameters is very complex to be implemented numerically. The results indicate that FSSN mixture model has a better performance to represent the data pattern of brain tumor MRI, more parsimony, and able to detect the brain tumor more precisely than the original GMM approach

Otimização da produção de energia elétrica em usinas hidrelétricas

Given the current scenario of greater concern regarding sustainable development, renewable energy sources have become increasingly important. Within this context, in addition to contributing to the greatest share of Brazilian renewable generation, hydroelectric power plants are responsible for most of the energy matrix in general. Since different operation strategies can be applied to a power plant’s set of turbines to supply the same power demand, it is presented in this work an investigation of computational intelligence techniques applied to the optimization of the operation of hydroelectric power plants. Such a study is motivated by the potential to save hydro and financial resources, hence contributing to a better operation planning of the electric system, and also by the simplicity in implementation, and effectiveness that intelligent techniques have presented over the years. The following contributions are proposed: (i) an application not yet explored in the literature involving binary computational intelligence techniques, which, basically, benefits from binary algorithms consolidated in the literature to solve problems whose decision variables are integer; (ii) an adaptation of a well known hydroelectric power plants model which allows the maximum saving of hydro resources through a simple modification in the methodology; and (iii) an innovative approach to the fitting of hydraulic turbines efficiency curves, which aims for a more accurate modeling of this component by applying techniques based on the machine learning concept. Simulations were performed with data of a power plant belonging to the EDP group. Adaptations of the Grey Wolf Optimizer and Sine Cosine Algorithm, and the Ant Colony Optimization algorithm have shown to be highly propitious in solving the given problem when duly configured, given the fact that these were able to reliably providing operation schedules that correspond to global optimum.Diante do cenário atual de maior preocupação com o desenvolvimento sustentável, fontes renováveis de energia têm se tornado cada vez mais importantes. Neste contexto, além de contribuírem com a maior parcela da geração renovável brasileira, usinas hidrelétricas são responsáveis pela maior parte da matriz energética em geral. Como diferentes estratégias de operação podem ser aplicadas a um conjunto de turbinas de uma usina para atender a mesma demanda de potência, este trabalho apresenta uma investigação de técnicas de inteligência computacional aplicadas à otimização da operação diária de usinas hidrelétricas. Tal estudo é motivado pelo potencial em se poupar recursos hídricos e financeiros, contribuindo assim para um melhor planejamento da operação do sistema elétrico, e também pela simplicidade de implementação e eficácia que técnicas de inteligência computacional têm apresentado ao longo dos anos. As seguintes contribuições são propostas: (i) uma aplicação até então não explorada na literatura envolvendo técnicas de inteligência computacional binárias, a qual, basicamente, se beneficia de algoritmos binários consolidados na literatura para solucionar problemas cujas variáveis de decisão são inteiras; (ii) uma adaptação de um modelo renomado de usinas hidrelétricas que viabiliza a economia máxima de recursos hídricos através de uma simples alteração na metodologia; e (iii) uma abordagem inovadora do ajuste de curvas de eficiência de turbinas hidráulicas, a qual almeja uma modelagem mais precisa de tal componente ao se aplicar técnicas baseadas no conceito de aprendizagem de máquinas. Simulações foram realizadas com dados de uma usina hidrelétrica pertencente ao grupo EDP. Adaptações dos algoritmos Grey Wolf Optimizer e Sine Cosine Algorithm e o algoritmo Ant Colony Optimization se mostraram altamente propícios a solucionar o problema em questão quando adequadamente configurados, visto que foram capazes de confiavelmente fornecer cronogramas de operação que correspondem a ótimos globais

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world