Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models.

Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality. Aiming to counter this issue, this research draws on the advancements in machine learning techniques in other fields for the generation of artificial data. The generated methods were tested through the evaluation of their output against that of a real-world labelled MRS brain tumor data-set. Furthermore the resultant output from the generative techniques were each used to train separate traditional classifiers which were tested on a subset of the real MRS brain tumor dataset. The results suggest that there exist methods capable of producing accurate, ground truth based MRS voxels. These findings indicate that through generative techniques, large datasets can be made available for training deep, learning models for the use in brain tumor diagnosis

A review on a deep learning perspective in brain cancer classification

AWorld Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, andWilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm

Análise de dados de espectroscopia de ressonância magnética utilizando aprendizado de máquina na tentativa de auxiliar na predição de farmacorresistência de pacientes de epilepsia do lobo temporal mesial

Orientador: Gabriela CastellanoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb WataghinResumo: Epilepsia do Lobo Temporal Mesial é o tipo de epilepsia mais frequente em adultos. Uma das causas associadas é a esclerose hipocampal, uma atrofia que consiste em perda de células nervosas do hipocampo. O tratamento inicia-se com a aplicação de drogas anti-epilépticas e pacientes que não possuem uma resposta adequada a esses medicamentos (farmacorresistentes) são candidatos aos procedimentos cirúrgicos. Aproximadamente um terço dos pacientes não possuem uma resposta adequada a medicamentos anti-epilépticos. O objetivo deste estudo é verificar se aprendizado de máquina, um ramo da inteligência artificial em que algoritmos encontram padrões por meio de dados fornecidos, pode ser aplicado na tentativa de prever pacientes portadores de epilepsia temporal mesial que possuem resistência às drogas anti-epilépticas, a partir de dados de espectroscopia de ressonância magnética. Um conjunto de dados de 115 pacientes com epilepsia temporal mesial (que posteriormente foi reduzido a 107 por critérios de exclusão) contendo indivíduos com e sem esclerose hipocampal foram obtidos de um projeto maior. Os metabólitos N-acetil-aspartato, colina, mio-inositol e o grupo formado por glutamato e glutamina foram quantificados dos espectros de ambos hipocampos e normalizados com relação a quantificação de creatina. Três abordagens de construção de vetores de atributos foram testadas: sobreposição da quantificação do lado com esclerose hipocampal, inclusão de duas variáveis para representar a presença ou não de esclerose no lado direto e esquerdo, e utilização da quantificação apenas do hipocampo com esclerose como vetor de atributos. Três algoritmos de aprendizado de máquina, Regressão Logística, Florestas Randômicas e Máquinas de Vetores de Suporte, foram explorados variando hiperparâmetros na busca pela combinação que resultasse em maior desempenho, medido pela área sob a curva (AUC) das respectivas curvas ROC (do inglês, receiver operating characteristic) e avaliado utilizando validação cruzada. Os melhores resultados obtidos foram utilizando somente a quantificação do hipocampo com esclerose como vetor de características, com os algoritmos Máquinas de Vetores de Suporte com o kernel RBF (5 folds, Gama=100 e C=0.01) e com o kernel sigmoidal (10 folds, Gama=0.01, Kappa=10, C=100) e Regressão Logística (10 folds, regularização L1 e C=1), que resultaram em AUC’s de 0.7 ± 0.1, e com os algoritmos Florestas Randômicas utilizando o critério de entropia (10 folds, 10 árvores e profundidade de 10 nodos) e utilizando o índice de Gini (10 folds, 20 árvores e máximo de 70 nodos) e Regressão Logística com regularização L2 (10 folds e C=0.1) obtendo uma AUC de 0.7 ± 0.2. Estes resultados indicam que esses algoritmos têm potencial para a previsão de farmacorresitência de pacientes portadores de epilepsia do lobo temporal mesial a partir concentração metabólica obtida por meio da espectroscopia de ressonância magnética dos hipocampos desses pacientesAbstract: Mesial Temporal Lobe Epilespy is the most frequently observed epilepsy in adults. One of the associated causes is hippocampal sclerosis, an atrophy that consists of loss of nerve cells in the hippocampus. Treatment begins with the application of anti-epileptic drugs, and patients who do not have an adequate response to this these drugs (pharmacoresistant) are candidates for surgical procedure. Approximately one third of patients do not have an adequate response to anti-epileptic drugs. The aim of this study is to verify whether machine learning, a branch of artificial intelligence in which algorithms find patterns through data, can be applied in an attempt to predict mesial temporal lobe epilepsy patients who are resistant to anti-epileptic drugs, using magnetic resonance spectroscopy data. A dataset of 115 mesial temporal lobe epilepsy patients (which was later reduced to 107 by exclusion criteria) containing individuals with and without hippocampal sclerosis was obtained from a larger project. The metabolites N-Acetyl-Aspartate, choline, myo-Inositol, and the glutamate and glutamine group were quantified from the spectra on both hippocampi and normalized in relation to the creatine quantification. Three approaches of feature vector construction were tested: superposition of the quantified side with hippocampus sclerosis, inclusion of two variables to represent the presence or not of hippocampal sclerosis in the right and left sides, and using only the quantification of the hippocampus with sclerosis as feature vector. Three machine learning algorithms, Logistic Regression, Random Forests and Support Vector Machines, were explored by varying hyperparameters in the search for the combination that would result in the best performance, measured by the area under the curve (AUC) of the respective ROC (receiver operating characteristic) curves and evaluated using crossvalidation. The best results were obtained using only the quantification of the side with hippocampal sclerosis, with the algorithms Support Vector Machines with the RBF kernel (5 folds, gamma=100 and C=0.01) and the sigmoidal kernel (10 folds, gamma=0.01, kappa=10, C=100) and Logistic Regression (10 folds, L1 Regularization and C=1), that resulted in an AUC of 0.7 ± 0.1, and with the algorithms Random Forests with the entropy criteria (10 folds, 10 trees and maximum depth of 10 nodes) and using the Gini index (10 folds, 20 trees and maximum depth of 70 nodes) and Logistic Regression with L2 regularization (10 folds and C=0.1) obtaining an AUC of 0.7 ± 0.2. These results indicate that these algorithms have the potential to predict the pharmacoresistance of patients with mesial temporal lobe epilepsy from the metabolic concentration obtained by magnetic resonance spectroscopy of the hippocampi of these patientsMestradoFísica AplicadaMestre em Física1765883/2017CAPE