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

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

Identification of hip fracture patients from radiographs using Fourier analysis of the trabecular structure: a cross-sectional study

Peer reviewedPublisher PD

WISDoM: Wishart Distributed Matrices Multiple Order classification. Definition and application to fMRI resting state data.

In this work we introduce the Wishart Distributed Matrices Multiple Order Classification (WISDoM) method. The WISDoM Classification method consists of a pipeline for single feature analysis, supervised learning,cross validation and classification for any problems whose elements can be tied to a symmetric positive-definite matrix representation. The general idea is for informations about properties of a certain system contained in a symmetric positive-definite matrix representation (i.e covariance and correlation matrices) to be extracted by modelling an estimated distribution for the expected classes of a given problem. The application to fMRI data classification and clustering processing follows naturally: the WISDoM classification method has been tested on the ADNI2 (Alzheimer's Disease Neuroimaging Initiative) database. The goal was to achieve good classification performances between Alzheimer's Disease diagnosed patients (AD) and Normal Control (NC) subjects, while retaining informations on which features were the most informative decision-wise. In our work, the informations about topological properties contained in ADNI2 functional correlation matrices are extracted by modelling an estimated Wishart distribution for the expected diagnostical groups AD and NC, and allowed a complete separation between the two groups

Evaluation of recurrent glioma and Alzheimer’s disease using novel multimodal brain image processing and analysis

Novel analysis techniques were applied to two different sets of multi-modality brain images. Localised metabolic rate within the hippocampus was assessed for its ability to differentiate between groups of healthy, mildly cognitively impaired, and Alzheimer’s disease brains, and an investigation of its potential clinical diagnostic utility was conducted. Relative uptake and retention of two PET tracers (11Carbon Methionine and 18Fluoro Thymidine) in a post-treatment glioma patient cohort was utilized to perform survival prediction analysis

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Characterizing and revealing biomarkers on patients with Cerebral Amyloid Angiopathy using artificial intelligence

Dissertação de mestrado em BioinformáticaCerebral Amyloid Angiopathy is a cerebrovascular disorder resulting from the deposition of an amyloidogenic protein in small and medium sized cortical and leptomeningeal vessels. A primary cause of spontaneous intracerebral haemorrhages, it manifests predominantly in the elder population. Although CAA is a common neuropathological finding on itself, it is also known to frequently occur in conjunction with Alzheimer’s disease, being sometimes misdiagnosed. Currently, CAA diagnosis is generally conducted by post-mortem examination or, in live patients by the examination of an evacuated hematoma or brain biopsy samples, which are typically unavailable. Therefore, a reliable and non-invasive method for diagnosing CAA would facilitate the clinical decision making and accelerate the clinical intervention. The main goal of this dissertation is to study the application of Machine Learning (ML) to reveal possible biomarkers to aid the diagnosis and early medical intervention, and better understand the disease. Therefore, three scenarios were tested: Classification of four neurodegenerative diseases with annotation data obtained from visual rating scores, age and gender; Classification of the diseases with radiomic data derived from the patient’s MRI; and a combination of the previous experiments. The results show that the application of Artificial intelligence in the medical field brings advantages to support the physicians in the decision making process and, at some point, make a correct prediction of the disease label. Although the results are satisfactory, there are still improvements to be done. For instance, image segmentation of cerebral lesions or brain regions and additional clinical information of the patients would be of value.Angiopatia Amiloide Cerebral (AAC) é uma doença vascular cerebral resultante da deposição de matéria amiloide. Principal causa de hemorragias cerebral espontâneas, a AAC manifesta se predominantemente na população idosa. Embora a AAC seja uma doença que por si só tem um grande impacto no grupo etário referido, ocorre em simultâneo com inúmeras outras doenças neurodegenerativas, como a doença de Alzheimer. Atualmente, o diagnóstico de AAC realiza-se quer em post-mortem, quer em pacientes vivos. No entanto, o diagnóstico em vida é conseguido por meio de biópsias de tecidos cerebrais, sendo um método invasivo, o que dificulta a intervenção clínica. Deste modo, torna-se imperativa a procura de alternativas fiáveis e não invasivas em vida para auxiliar o diagnóstico da doença e permitir a melhoria da qualidade de vida do paciente. Perante os progressos na área da tecnologia e medicina, esta dissertação propõe o estudo da aplicação de algoritmos de Machine Learning (ML) para revelar possíveis biomarcadores para auxiliar o diagnóstico e permitir uma intervenção precoce. Deste modo, foram testados três cenários distintos: a classificação de quatro doenças neurodegenerativas com dados anotados obtidos a partir de métricas visuais de avaliação da atrofia, idade e sexo; a classificação das doenças com dados gerados a partir de métodos radiómicos; e uma combinação das duas abordagens anteriores. Neste documento apresenta-se e discute-se os resultados obtidos com a aplicação de quatro diferentes algoritmos de ML que visam a deteção automática da doença associada à imagem testada. Adicionalmente, é feita uma análise crítica de quais as características mais relevantes que levaram à tomada de decisão por parte do algoritmo. Os resultados demonstram que através de aplicação de metodologias automáticas é possível o auxílio ao diagnostico médico por especialistas e, no limite, a realização de diagnostico automático com elevada precisão. Finalmente, são apresentadas possíveis alternativas de trabalho futuro para que os resultados possam ser aperfeiçoados, como por exemplo, a segmentação das regiões de interesse, i.e., identificação das lesões, aquando da anotação por especialistas. Mediante a inclusão dessa segmentação, uma vez que será mais especifica, os resultados serão, por sua vez, aprimorados