214 research outputs found

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

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    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%

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

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    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

    Contributions in computational intelligence with results in functional neuroimaging

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    This thesis applies computational intelligence methodologies to study functional brain images. It is a state-of-the-art application relative to unsupervised learning domain to functional neuroimaging. There are also contributions related to computational intelligence on topics relative to clustering validation and spatio-temporal clustering analysis. Speci_cally, there are the presentation of a new separation measure based on fuzzy sets theory to establish the validity of the fuzzy clustering outcomes and the presentation of a framework to approach the parcellation of functional neuroimages taking in account both spatial and temporal patterns. These contributions have been applied to neuroimages obtained with functional Magnetic Resonance Imaging, using both active and passive paradigm and using both in-house data and fMRI repository. The results obtained shown, globally, an improvement on the quality of the neuroimaging analysis using the methodological contributions proposed

    MRI Deep Learning-Based Solution for Alzheimer’s Disease Prediction

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    Background: Alzheimer’s is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Al though tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis. Methods: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer’s diagnosis is proposed and compared with previous literature works. Results: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage). Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer’s-assisted diagnosis based on MRI data.This work was partially supported by the SUPREME project. This project has received funding from the Basque Government’s Industry Department HAZITEK program under agreement ZE-2019/00022. This research has also received funding from the Basque Government’s Industry Department under the ELKARTEK program’s project ONKOTOOLS under agreement KK-2020/0006

    Mathematical modeling and visualization of functional neuroimages

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    Metabolic spatial connectivity in Amyotrophic Lateral Sclerosis as revealed by independent component analysis

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    Objectives. Positron emission tomography (PET) and volume of interest (VOI) analysis have recently shown in amyotrophic lateral sclerosis (ALS) an accuracy of 93% in differentiating patients from controls. The aim of this study was to disclose by spatial independent component analysis (ICA) the brain networks involved in ALS pathological processes and evaluate their discriminative value in separating patients from controls. Experimental design. Two hundred fifty-nine ALS patients and 40 age- and sex-matched control subjects underwent brain 18F-2-fluoro-2-deoxy-D-glucose PET (FDG-PET). Spatial ICA of the preprocessed FDG-PET images was performed. Intensity values were converted to z-scores and binary masks were used as data-driven VOIs. The accuracy of this classifier was tested versus a validated system processing intensity signals in 27 brain meta-VOIs. A support vector machine was independently applied to both datasets and the \u27leave-one-out\u27 technique verified the general validity of results. Principal observations: The 8 components selected as pathophysiologically meaningful discriminated patients from controls with 99.0% accuracy, the discriminating value of bilateral cerebellum/midbrain alone representing 96.3%. Among the meta-VOIs, right temporal lobe alone reached an accuracy of 93.7%. Conclusions: Spatial ICA identified in a very large cohort of ALS patients distinct spatial networks showing a high discriminatory value, improving substantially on the previously obtained accuracy. The cerebellar/midbrain component accounted for the highest accuracy in separating ALS patients from controls. Spatial ICA and multivariate analysis perform better than univariate semi-quantification methods in identifying the neurodegenerative features of ALS and pave the way for inclusion of PET in clinical trials and early diagnosis
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