Computer-Aided Diagnosis for Early Identification of Multi-Type Dementia using Deep Neural Networks

With millions of people suffering from dementia worldwide, the global prevalence of this condition has a significant impact on the global economy. As well, its prevalence has a negative impact on patients’ lives and their caregivers’ physical and emotional states. Dementia can be developed as a result of some risk factors as well as it has many forms whose signs are sometimes similar. While there is currently no cure for dementia, effective early diagnosis is essential in managing it. Early diagnosis helps people in finding suitable therapies that reduce or even prevent further deterioration of cognitive abilities, and in taking control of their conditions and planning for the future. Furthermore, it also facilitates the research efforts to understand causes and signs of dementia. Early diagnosis is based on the classification of features extracted from three-dimensional brain images. The features have to accurately capture main dementia-related anatomical variations of brain structures, such as hippocampus size, gray and white matter tissues’ volumes, and brain volume. In recent years, numerous researchers have been seeking the development of new or improved Computer-Aided Diagnosis (CAD) technologies to accurately detect dementia. The CAD approaches aim to assist radiologists in increasing the accuracy of the diagnosis and reducing false positives. However, there is a number of limitations and open issues in the state-of-the-art, that need to be addressed. These limitations include that literature to date has focused on differentiating multi-stage of Alzheimer’s disease severity ignoring other dementia types that can be as devastating or even more. Furthermore, the high dimensionality of neuroimages, as well as the complexity of dementia biomarkers, can hinder classification performance. Moreover, the augmentation of neuroimaging analysis with contextual information has received limited attention to-date due to the discrepancies and irregularities of the various forms of data. This work focuses on addressing the need for differentiating between multiple types of dementia in early stages. The objective of this thesis is to automatically discriminate normal controls from patients with various types of dementia in early phases of the disease. This thesis proposes a novel CAD approach, integrating a stacked sparse auto-encoder (SSAE) with a two- dimensional convolutional neural network (CNN) for early identification of multiple types of dementia based on using the discriminant features, extracted from neuroimages, incorporated with the context information. By applying SSAE to intensities extracted from magnetic resonance (MR) neuroimages, SSAE can reduce the high dimensionality of neuroimages and learn changes, exploiting important discrimination features for classification. This research work also proposes to integrate features extracted from MR neuroimages with patients’ contextual information through fusing multi-classifier to enhance the early prediction of various types of dementia. The effectiveness of the proposed method is evaluated on OASIS dataset using five different relevant performance metrics, including accuracy, f1-score, sensitivity, specificity, and precision-recall curve. Across a cohort of 4000 MR neuroimages (176 × 176) as well as the contextual information, and clinical diagnosis of patients serving as the ground truth, the proposed CAD approach was shown to have an improved F-measure of 93% and an average area under Precision-Recall curve of 94%. The proposed method provides a significant improvement in classification output, resulted in high and reproducible accuracy rates of 95% with a sensitivity of 93%, and a specificity of 88%

Advances in Statistical and Machine Learning Methods for Image Data, with Application to Alzheimer's Disease

The revolutionary development of neuroimage technology allows for the generation of large-scale neuroimage data in modern medical studies. For example, structural magnetic resonance imaging (sMRI) is widely used in segmenting neurodegenerative regions in the brain and positron-emission tomography (PET) is commonly used by clinicians and researchers to quantify the severity of Alzheimer's disease. In the first part of this dissertation, we build “OASIS-AD”, which is a supervised learning model based on a well-validated automated segmentation tool “OASIS” in multiple sclerosis (MS). OASIS-AD considers the specific challenges raised by WMH in Alzheimer's Disease (AD) to reduce false discoveries. We show that OASIS-AD performs better than several existing automated white matter hyperintensity segmentation approaches. In the second part of this dissertation, we develop an interpretable penalized multivariate high-dimensional method for image-on-scalar regression that can be used for association studies between high-dimensional PET images and patients' scalar measures. This method overcomes the lack of interpretability in regularized regression after reduced-rank decomposition through a novel encoder-decoder based penalty to regularize interpretable image characteristics. Empirical properties of the proposed approach are examined and compared to existing methods in simulation studies and in the analysis of PET images from subjects in a study of Alzheimer's Disease. In the third part of this dissertation, we developed ACU-Net, an efficient convolutional network for medical image segmentation. The proposed deep learning network overcomes the small sample size problem of training a deep neural network when used for medical image segmentation. It also decreases computation cost by increasing the effective degrees of freedom through data augmentation and the novel use of convolutional layers blocks to compress the model. We show that ACU-Net can achieve competitive performance while dramatically decreases the computation cost compared with modern CNNs. Public health significance: This dissertation proposes new statistical and machine learning methods for two aging-related problems: (1) automatically segmenting white matter hyperintensity (WMH), a biomarker of neurodegenerative pathology, and (2) estimating the association between neurodegeneration pathology and vascular measures, which are important to aging population living quality and can be studied by clinical neuroimage data

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

Optimisation of statistical methodologies for a better diagnosis of neurological and psychiatric disorders by means of SPECT

In the last years there has been a wide consensus on the importance of brain imaging in assessing neurodegenerative and psychiatric disorders. Different techniques for functional and anatomical examination are currently clinically implemented in neurology and psychiatry to improve sensitivity, specificity and accuracy of the diagnosis of various diseases. In addition, the increasing life expectancy in the Western world raises the social importance and the economical impact of age-related neurodegenerative disorders since the incidence of Alzheimer disease and Parkinson disease is higher in the elderly. An early diagnosis of neuro-psychiatric diseases and the assessment of "natural" changes of regional cerebral blood flow (rCBF) distribution during normal aging are hence of utmost importance. In the recent past brain disorders have extensively been investigated by means of optimised nuclear medicine techniques, instruments and algorithms. Diagnosis can be better achieved by identifying those structures in which CBF or metabolism deviate from normality resulting in significant changes as compared to a reference database. In the present paper we present some studies investigating, by means of recently implemented diagnostic tools, patients bearer of various neuro-psychiatric disorders. The improved nuclear medicine techniques and instrumentation, the state-of-the-art software for brain imaging standardisation and the use of sophisticated multivariate data analysis are extensively reviewed

An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images

Objective: This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. Methods: For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. Results: Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602 pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%). (C) 2017 Published by Elsevier Ireland Ltd.FCTIDMECLAETA [UID/EMS/50022/2013

Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

Functional magnetic resonance imaging (fMRI) is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD) in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities

Morphometric data fusion for early detection of alzheimer’s disease

Abstract. We present a morphometry method which uses brain models generated using Nonnegative Matrix Factorization (NMF) characterized by signatures calculated from perceptual features such as intensities, edges and orientations, of some regions obtained by comparing the models. Two different measures are used to calculate volume-models distances in the regions of interest. The discerning power of these distances is tested by using them as features for a Support Vector Machine classifier. This work shows the usefulness of both measures as metrics in medical image applications when they are used in binary classification tasks. Our methodology was tested with two experimental groups extracted from a public brain MR dataset (OASIS), the classification between healthy subjects and patients with mild AD reveals an equal error rate (EER) measure which is better than previous approaches tested on the same dataset (0.1 in the former and 0.2 in the latter). When detecting very mild AD, our results (near to 75% of sensitivity and specificity) are comparable to the results with those approaches.Presentamos un m´etodo de morfometr´ı que usa modelos de cerebro que se generan usando factorizaci´on de matrices no-negativas (NMF por su nombre en ingl´es) y se caracterizan por firmas calculadas de rasgos perceptules como las intensidades, bordes y orientaciones de algunas regiones del cerebro obtenidas de la comparaci´on entre modelos. Dos medidas, la divergencia de Kullback-Leibler y la “Earth Mover’s Distance”, son usadas para calcular la distancia entre vol´umenes y modelos en las regiones de inter´es. Probamos el poder discriminante de estas distancias us´andolas para construir los vectores de caracter´ısticas para una m´aquina de soporte vectorial. Este trabajo muestra la utilidad de ambas medidas en tareas de clasificaci´on binaria. Nuestra metodolog´ıa fue probada con dos grupos experimentales extra´ıdos de la base de datos OASIS, la clasificaci´on entre sujetos sanos y pacientes con Alzheimer leve revela un EER que mejora los resultados obtenidos por trabajos publicados previamente con los mismos grupos experimentales. Cuando se trata de detectar Alzheimer muy leve, los resultados (cercanos a 75% de sensibilidad y especificidad) son comparables con los resultados obtenidos en dichas publicaciones.Maestrí

Doctor of Philosophy

dissertationNeurodegenerative diseases are an increasing health care problem in the United States. Quantitative neuroimaging provides a noninvasive method to illuminate individual variations in brain structure to better understand and diagnose these disorders. The overall objective of this research is to develop novel clinical tools that summarize and quantify changes in brain shape to not only help better understand age-appropriate changes but also, in the future, to dissociate structural changes associated with aging from those caused by dementing neurodegenerative disorders. Because the tools we will develop can be applied for individual assessment, achieving our goals could have a significant clinical impact. An accurate, practical objective summary measure of the brain pathology would augment current subjective visual interpretation of structural magnetic resonance images. Fractal dimension is a novel approach to image analysis that provides a quantitative measure of shape complexity describing the multiscale folding of the human cerebral cortex. Cerebral cortical folding reflects the complex underlying architectural features that evolve during brain development and degeneration including neuronal density, synaptic proliferation and loss, and gliosis. Building upon existing technology, we have developed innovative tools to compute global and local (voxel-wise and regional) cerebral cortical fractal dimensions and voxel-wise cortico-fractal surfaces from high-contrast MR images. Our previous research has shown that fractal dimension correlates with cognitive function and changes during the course of normal aging. We will now apply unbiased diffeomorphic atlasing methodology to dramatically improve the alignment of complex cortical surfaces. Our novel methods will create more accurate, detailed geometrically averaged images to take into account the intragroup differences and make statistical inferences about spatiotemporal changes in shape of the cerebral cortex across the adult human lifespan