Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Classification of dementias based on brain radiomics features

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

A four-dimensional probabilistic atlas of the human brain

The authors describe the development of a four-dimensional atlas and reference system that includes both macroscopic and microscopic information on structure and function of the human brain in persons between the ages of 18 and 90 years. Given the presumed large but previously unquantified degree of structural and functional variance among normal persons in the human population, the basis for this atlas and reference system is probabilistic. Through the efforts of the International Consortium for Brain Mapping (ICBM), 7,000 subjects will be included in the initial phase of database and atlas development. For each subject, detailed demographic, clinical, behavioral, and imaging information is being collected. In addition, 5,800 subjects will contribute DNA for the purpose of determining genotype-phenotype-behavioral correlations. The process of developing the strategies, algorithms, data collection methods, validation approaches, database structures, and distribution of results is described in this report. Examples of applications of the approach are described for the normal brain in both adults and children as well as in patients with schizophrenia. This project should provide new insights into the relationship between microscopic and macroscopic structure and function in the human brain and should have important implications in basic neuroscience, clinical diagnostics, and cerebral disorders

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Applications of Artificial Intelligence in Medicine Practice

This book focuses on a variety of interdisciplinary perspectives concerning the theory and application of artificial intelligence (AI) in medicine, medically oriented human biology, and healthcare. The list of topics includes the application of AI in biomedicine and clinical medicine, machine learning-based decision support, robotic surgery, data analytics and mining, laboratory information systems, and usage of AI in medical education. Special attention is given to the practical aspect of a study. Hence, the inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly highlighted

Abordagem CNN 2D estendida para o diagnóstico da doença de Alzheimer através de imagens de ressonância magnética estrutural

Orientadores: Leticia Rittner, Roberto de Alencar LotufoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A doença de Alzheimer (AD - Alzheimer's disease) é um tipo de demência que afeta milhões de pessoas em todo o mundo. Até o momento, não há cura para a doença e seu diagnóstico precoce tem sido uma tarefa desafiadora. As técnicas atuais para o seu diagnóstico têm explorado as informações estruturais da Imagem por Ressonância Magnética (MRI - Magnetic Resonance Imaging) em imagens ponderadas em T1. Entre essas técnicas, a rede neural convolucional (CNN - Convolutional Neural Network) é a mais promissora e tem sido usada com sucesso em imagens médicas para uma variedade de aplicações devido à sua capacidade de extração de características. Antes do grande sucesso do aprendizado profundo e das CNNs, os trabalhos que objetivavam classificar os diferentes estágios de AD exploraram abordagens clássicas de aprendizado de máquina e uma meticulosa extração de características, principalmente para classificar testes binários. Recentemente, alguns autores combinaram técnicas de aprendizagem profunda e pequenos subconjuntos do conjunto de dados públicos da Iniciativa de Neuroimagem da Doença de Alzheimer (ADNI - Alzheimer's Disease Neuroimaging Initiative) para prever um estágio inicial da doença explorando abordagens 3D CNN geralmente combinadas com arquiteturas de auto-codificador convolucional 3D. Outros também exploraram uma abordagem de CNN 3D combinando-a ou não com uma etapa de pré-processamento para a extração de características. No entanto, a maioria desses trabalhos focam apenas na classificação binária, sem resultados para AD, comprometimento cognitivo leve (MCI - Mild Cognitive Impairment) e classificação de sujeitos normais (NC - Normal Control). Nosso principal objetivo foi explorar abordagens de CNN 2D para a tarefa de classificação das 3 classes usando imagens de MRI ponderadas em T1. Como objetivo secundário, preenchemos algumas lacunas encontradas na literatura ao investigar o uso de arquiteturas CNN 2D para o nosso problema, uma vez que a maioria dos trabalhos explorou o aprendizado de máquina clássico ou abordagens CNN 3D. Nossa abordagem CNN 2D estendida explora as informações volumétricas dos dados de ressonância magnética, mantendo baixo custo computacional associado a uma abordagem 2D, quando comparados às abordagens 3D. Além disso, nosso resultado supera as outras estratégias para a classificação das 3 classes e comparando o desempenho de nosso modelo com os métodos tradicionais de aprendizado de máquina e 3D CNN. Também investigamos o papel de diferentes técnicas amplamente utilizadas em aplicações CNN, por exemplo, pré-processamento de dados, aumento de dados, transferência de aprendizado e adaptação de domínio para um conjunto de dados brasileiroAbstract: Alzheimer's disease (AD) is a type of dementia that affects millions of people around the world. To date, there is no cure for Alzheimer's and its early-diagnosis has been a challenging task. The current techniques for Alzheimer's disease diagnosis have explored the structural information of Magnetic Resonance Imaging (MRI) in T1-weighted images. Among these techniques, deep convolutional neural network (CNN) is the most promising one and has been successfully used in medical images for a variety of applications due to its ability to perform features extraction. Before the great success of deep learning and CNNs, the works that aimed to classify the different stages of AD explored classic machine learning approaches and a meticulous feature engineering extraction, mostly to classify binary tasks. Recently, some authors have combined deep learning techniques and small subsets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) public dataset, to predict an early-stage of AD exploring 3D CNN approaches usually combined with 3D convolutional autoencoder architectures. Others have also investigated a 3D CNN approach combining it or not with a pre-processing step for the extraction of features. However, the majority of these papers focus on binary classification only, with no results for Alzheimer's disease, Mild Cognitive Impairment (MCI), and Normal Control (NC) classification. Our primary goal was to explore 2D CNN approaches to tackle the 3-class classification using T1-weighted MRI. As a secondary goal, we filled some gaps we found in the literature by investigating the use of 2D CNN architectures to our problem, since most of the works either explored traditional machine learning or 3D CNN approaches. Our extended-2D CNN explores the MRI volumetric data information while maintaining the low computational costs associated with a 2D approach when compared to 3D-CNNs. Besides, our result overcomes the other strategies for the 3-class classification while analyzing the performance of our model with traditional machine-learning and 3D-CNN methods. We also investigated the role of different widely used techniques in CNN applications, for instance, data pre-processing, data augmentation, transfer-learning, and domain-adaptation to a Brazilian datasetMestradoEngenharia de ComputaçãoMestra em Engenharia Elétrica168468/2017-4  CNP

Developing multi-modal analysis of brain anatomical connectivity using quantitative MRI data

The aim of this work is to develop a method to investigate connectivity properties of a specific set of Grey Matter regions in the brain, and its relationship with connectivity described by the fibre structure of underlying White Matter. Data were acquired with MRI, a flexible, widely available, non-invasive technique, which can picture in vivo the connectome, a comprehensive map of neural connections in the living brain. We worked with scans of healthy subjects and patients affected by M

Focal Spot, Spring/Summer 1985

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