10 research outputs found
Deep Learning for Multiclass Classification, Predictive Modeling and Segmentation of Disease Prone Regions in Alzheimer’s Disease
One of the challenges facing accurate diagnosis and prognosis of Alzheimer’s Disease (AD) is identifying the subtle changes that define the early onset of the disease. This dissertation investigates three of the main challenges confronted when such subtle changes are to be identified in the most meaningful way. These are (1) the missing data challenge, (2) longitudinal modeling of disease progression, and (3) the segmentation and volumetric calculation of disease-prone brain areas in medical images. The scarcity of sufficient data compounded by the missing data challenge in many longitudinal samples exacerbates the problem as we seek statistical meaningfulness in multiclass classification and regression analysis. Although there are many participants in the AD Neuroimaging Initiative (ADNI) study, many of the observations have a lot of missing features which often lead to the exclusion of potentially valuable data points that could add significant meaning in many ongoing experiments. Motivated by the necessity of examining all participants, even those with missing tests or imaging modalities, multiple techniques of handling missing data in this domain have been explored. Specific attention was drawn to the Gradient Boosting (GB) algorithm which has an inherent capability of addressing missing values. Prior to applying state-of-the-art classifiers such as Support Vector Machine (SVM) and Random Forest (RF), the impact of imputing data in common datasets with numerical techniques has been also investigated and compared with the GB algorithm. Furthermore, to discriminate AD subjects from healthy control individuals, and Mild Cognitive Impairment (MCI), longitudinal multimodal heterogeneous data was modeled using recurring neural networks (RNNs). In the segmentation and volumetric calculation challenge, this dissertation places its focus on one of the most relevant disease-prone areas in many neurological and neurodegenerative diseases, the hippocampus region. Changes in hippocampus shape and volume are considered significant biomarkers for AD diagnosis and prognosis. Thus, a two-stage model based on integrating the Vision Transformer and Convolutional Neural Network (CNN) is developed to automatically locate, segment, and estimate the hippocampus volume from the brain 3D MRI. The proposed architecture was trained and tested on a dataset containing 195 brain MRIs from the 2019 Medical Segmentation Decathlon Challenge against the manually segmented regions provided therein and was deployed on 326 MRI from our own data collected through Mount Sinai Medical Center as part of the 1Florida Alzheimer Disease Research Center (ADRC)
XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016)
En la presente edición, más de 150 trabajos de alto nivel científico van a ser presentados en 18 sesiones paralelas y 3 sesiones de póster, que se centrarán en áreas relevantes de la Ingeniería Biomédica. Entre las sesiones paralelas se pueden destacar la sesión plenaria Premio José María Ferrero Corral y la sesión de Competición de alumnos de Grado en Ingeniería Biomédica, con la participación de 16 alumnos de los Grados en Ingeniería Biomédica a nivel nacional.
El programa científico se complementa con dos ponencias invitadas de científicos reconocidos internacionalmente, dos mesas redondas con una importante participación de sociedades científicas médicas y de profesionales de la industria de tecnología médica, y dos actos sociales que permitirán a los participantes acercarse a la historia y cultura valenciana. Por primera vez, en colaboración con FENIN, seJane Campos, R. (2017). XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/79277EDITORIA
[<sup>18</sup>F]fluorination of biorelevant arylboronic acid pinacol ester scaffolds synthesized by convergence techniques
Aim: The development of small molecules through convergent multicomponent reactions (MCR) has been boosted during the last decade due to the ability to synthesize, virtually without any side-products, numerous small drug-like molecules with several degrees of structural diversity.(1) The association of positron emission tomography (PET) labeling techniques in line with the “one-pot” development of biologically active compounds has the potential to become relevant not only for the evaluation and characterization of those MCR products through molecular imaging, but also to increase the library of radiotracers available. Therefore, since the [18F]fluorination of arylboronic acid pinacol ester derivatives tolerates electron-poor and electro-rich arenes and various functional groups,(2) the main goal of this research work was to achieve the 18F-radiolabeling of several different molecules synthesized through MCR. Materials and Methods: [18F]Fluorination of boronic acid pinacol esters was first extensively optimized using a benzaldehyde derivative in relation to the ideal amount of Cu(II) catalyst and precursor to be used, as well as the reaction solvent. Radiochemical conversion (RCC) yields were assessed by TLC-SG. The optimized radiolabeling conditions were subsequently applied to several structurally different MCR scaffolds comprising biologically relevant pharmacophores (e.g. β-lactam, morpholine, tetrazole, oxazole) that were synthesized to specifically contain a boronic acid pinacol ester group. Results: Radiolabeling with fluorine-18 was achieved with volumes (800 μl) and activities (≤ 2 GBq) compatible with most radiochemistry techniques and modules. In summary, an increase in the quantities of precursor or Cu(II) catalyst lead to higher conversion yields. An optimal amount of precursor (0.06 mmol) and Cu(OTf)2(py)4 (0.04 mmol) was defined for further reactions, with DMA being a preferential solvent over DMF. RCC yields from 15% to 76%, depending on the scaffold, were reproducibly achieved. Interestingly, it was noticed that the structure of the scaffolds, beyond the arylboronic acid, exerts some influence in the final RCC, with electron-withdrawing groups in the para position apparently enhancing the radiolabeling yield. Conclusion: The developed method with high RCC and reproducibility has the potential to be applied in line with MCR and also has a possibility to be incorporated in a later stage of this convergent “one-pot” synthesis strategy. Further studies are currently ongoing to apply this radiolabeling concept to fluorine-containing approved drugs whose boronic acid pinacol ester precursors can be synthesized through MCR (e.g. atorvastatin)
Development of Anatomical and Functional Magnetic Resonance Imaging Measures of Alzheimer Disease
Alzheimer disease is considered to be a progressive neurodegenerative condition, clinically characterized by cognitive dysfunction and memory impairments. Incorporating imaging biomarkers in the early diagnosis and monitoring of disease progression is increasingly important in the evaluation of novel treatments. The purpose of the work in this thesis was to develop and evaluate novel structural and functional biomarkers of disease to improve Alzheimer disease diagnosis and treatment monitoring. Our overarching hypothesis is that magnetic resonance imaging methods that sensitively measure brain structure and functional impairment have the potential to identify people with Alzheimer’s disease prior to the onset of cognitive decline. Since the hippocampus is considered to be one of the first brain structures affected by Alzheimer disease, in our first study a reliable and fully automated approach was developed to quantify medial temporal lobe atrophy using magnetic resonance imaging. This measurement of medial temporal lobe atrophy showed differences (pnovel biomarker of brain activity was developed based on a first-order textural feature of the resting state functional magnetic resonance imagining signal. The mean brain activity metric was shown to be significantly lower (pp18F labeled fluorodeoxyglucose positron emission tomography. In the final study, we examine whether combined measures of gait and cognition could predict medial temporal lobe atrophy over 18 months in a small cohort of people (N=22) with mild cognitive impairment. The results showed that measures of gait impairment can help to predict medial temporal lobe atrophy in people with mild cognitive impairment. The work in this thesis contributes to the growing evidence the specific magnetic resonance imaging measures of brain structure and function can be used to identify and monitor the progression of Alzheimer’s disease. Continued refinement of these methods, and larger longitudinal studies will be needed to establish whether the specific metrics of brain dysfunction developed in this thesis can be of clinical benefit and aid in drug development
An Integrated Neuroimaging Approach for the Prediction and Analysis of Alzheimer’s Disease and its Prodromal Stages
This dissertation proposes to combine magnetic resonance imaging (MRI), positron emission tomography (PET) and a neuropsychological test, Mini-Mental State Examination (MMSE), as input to a multidimensional space for the classification of Alzheimer’s disease (AD) and it’s prodromal stages including amnestic MCI (aMCI) and non-amnestic MCI (naMCI). An assessment is provided on the effect of different MRI normalization techniques on the prediction of AD. Statistically significant variables selected for each combination model were used to construct the classification space using support vector machines. To combine MRI and PET, orthogonal partial least squares to latent structures is used as a multivariate analysis to discriminate between AD, early and late MCI (EMCI and LMCI) from cognitively normal (CN)s. In addition, this dissertation proposes a new effective mean indicator (EMI) method for distinguishing stages of AD from CN. EMI utilizes the mean of specific top-ranked measures, determined by incremental error analysis, to achieve optimal separation of AD and CN.
For AD vs. CN, the two most discriminative volumetric variables (right hippocampus and left inferior lateral ventricle), when combined with MMSE scores, provided an average accuracy of 92.4% (sensitivity: 84.0%; specificity: 96.1%). MMSE scores were found to improve classification accuracy by 8.2% and 12% for aMCI vs. CN and naMCI vs. CN, respectively. Brain atrophy was almost evenly seen on both sides of the brain for AD subjects, which was different from right side dominance for aMCI and left side dominance for naMCI. Findings suggest that subcortical volume need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or the mean thickness. Furthermore, MRI and PET had comparable predictive power in separating AD from CN. For the EMCI prediction, cortical thickness was found to be the best predictor, even better than using all features together. Validation with an external test set demonstrated that best of feature-selected models for the LMCI group was able to classify 83% of the LMCI subjects. The EMI-based method achieved an accuracy of 92.7% using only MRI features. The performance of the EMI-based method along with its simplicity suggests great potential for its use in clinical trials
Automated detection of depression from brain structural magnetic resonance imaging (sMRI) scans
Automated sMRI-based depression detection system is developed whose components include acquisition and preprocessing, feature extraction, feature selection, and classification. The core focus of the research is on the establishment of a new feature selection algorithm that quantifies the most relevant brain volumetric feature for depression detection at an individual level
Alzheimer's disease early detection from sparse data using brain importance maps
Statistical methods are increasingly used in the analysis of FDG-PET images for the early diagnosis of Alzheimer's disease. We will present a method to extract information about the location of metabolic changes induced by Alzheimer's disease based on a machine learning approach that directly links features and brain areas to search for regions of interest (ROIs). This approach has the advantage over voxel-wise statistics to also consider the interactions between the features/voxels. We produce "maps" to visualize the most informative regions of the brain and compare the maps created by our approach with voxel-wise statistics. In classification experiments, using the extracted map, we achieved classification rates of up to 95.5%
Differentiation of Alzheimer's disease dementia, mild cognitive impairment and normal condition using PET-FDG and AV-45 imaging : a machine-learning approach
Nous avons utilisé l'imagerie TEP avec les traceurs F18-FDG et AV45 en conjonction avec les méthodes de classification du domaine du "Machine Learning". Les images ont été acquises en mode dynamique, une image toutes les 5 minutes. Les données ont été transformées par Analyse en Composantes Principales et Analyse en Composantes Indépendantes. Les images proviennent de trois sources différentes: la base de données ADNI (Alzheimer's Disease Neuroimaging Initiative) et deux protocoles réalisés au sein du centre TEP de l'hôpital Purpan. Pour évaluer la performance de la classification nous avons eu recours à la méthode de validation croisée LOOCV (Leave One Out Cross Validation). Nous donnons une comparaison entre les deux méthodes de classification les plus utilisées, SVM (Support Vector Machine) et les réseaux de neurones artificiels (ANN). La combinaison donnant le meilleur taux de classification semble être SVM et le traceur AV45. Cependant les confusions les plus importantes sont entre les patients MCI et les sujets normaux. Les patients Alzheimer se distinguent relativement mieux puisqu'ils sont retrouvés souvent à plus de 90%. Nous avons évalué la généralisation de telles méthodes de classification en réalisant l'apprentissage sur un ensemble de données et la classification sur un autre ensemble. Nous avons pu atteindre une spécificité de 100% et une sensibilité supérieure à 81%. La méthode SVM semble avoir une meilleure sensibilité que les réseaux de neurones. L'intérêt d'un tel travail est de pouvoir aider à terme au diagnostic de la maladie d'Alzheimer.We used PET imaging with tracers F18-FDG and AV45 in conjunction with the classification methods in the field of "Machine Learning". PET images were acquired in dynamic mode, an image every 5 minutes.The images used come from three different sources: the database ADNI (Alzheimer's Disease Neuro-Imaging Initiative, University of California Los Angeles) and two protocols performed in the PET center of the Purpan Hospital. The classification was applied after processing dynamic images by Principal Component Analysis and Independent Component Analysis. The data were separated into training set and test set. To evaluate the performance of the classification we used the method of cross-validation LOOCV (Leave One Out Cross Validation). We give a comparison between the two most widely used classification methods, SVM (Support Vector Machine) and artificial neural networks (ANN) for both tracers. The combination giving the best classification rate seems to be SVM and AV45 tracer. However the most important confusion is found between MCI patients and normal subjects. Alzheimer's patients differ somewhat better since they are often found in more than 90%. We evaluated the generalization of our methods by making learning from set of data and classification on another set . We reached the specifity score of 100% and sensitivity score of more than 81%. SVM method showed a bettrer sensitivity than Artificial Neural Network method. The value of such work is to help the clinicians in diagnosing Alzheimer's disease
Classification of mild cognitive impairment and alzheimer's disease patients: a multiscale and multivariate approach
[ANGLÈS] Alzheimer's Disease (AD) is the most common form of dementia and a growing health and socioeconomic problem. Moreover, the impact of the disease is expected to increase even more as the life expectancy is going to grow over the years. Consequently, a lot of research is focused on computer-aided diagnosis techniques that aim at quantitatively study Magnetic Resonance brain images of early stage patients. Early diagnosis could help in better future cure or disease- modifying treatments. An example of AD early stage is Mild Cognitive Impariment (MCI), as the 50% of the individuals who suffer from this pathology develop AD in three of four years. In this work, we use Support Vector Machines to classify subjects from AD, MCI and healthy control (CTL) groups. Our main objective is to study whether combining different anatomical scale brain regions and different image modalities could improve the classification accuracy. Thus, regional and global Grey Matter (GM) volumes (multiscale approach), Withe Matter (WM) regional volumes, Regional Asymmetry coefficients and T1- quantitative MRI data (multivariate) are combined. Our accuracies when comparing CTL vs AD and CTL vs MCI with large public databases (ADNI) are comparable to the results in the literature: 88.3% and 81.8% respectively. In this master thesis we study also smaller databases of MCI patients from Lausanne University Hospital. We pay special attention to the study of pre-processing steps: Intra Craneal Volume normalization and age correction. Our results show that for our small group of patients, better accuracies can be obtained when combining different types of features (multiscale and multivariate) than when only using classical GM region volumes. Moreover, the new region-based age-correction method proposed here presents encouraging results when applied prior to both CTL vsMCI and CTL vs AD classification.[CASTELLÀ] La Enfermedad de Alzheimer (EA) es la forma más común de demencia y se ha convertido en un problema socioeconómico creciente. Además, se prevé que el impacto de la enfermedad será aún mayor dentro de unos años debido al progresivo envejecimiento de la población mundial y al crecimiento de la esperanza de vida. Es por estas razones que en los últimos años se ha centrado la atención en técnicas computarizadas para la diagnosis que están dirigidas al estudio cuantitativo de imágenes de resonancia magnética (MRI) de cerebro de pacientes que se encuentran en una etapa temprana de la enfermedad. Un diagnóstico precoz podría mejorar la efectividad de los futuros tratamientos de curación o modificación del curso natural de la enfermedad. Un ejemplo de etapa temprana de EA es el Deterioro Cognitivo Ligero (Mild Cognitive Impariment o MCI), puesto que el 50% de los pacientes que padecen esta patología desarrollan EA en tres o cuatro años. En este estudio, usamos Support Vector Machines para clasificar sujetos de tres grupos diferentes: EA, MCI y sujetos sanos de control (CTL). Nuestro objetivo es estudiar si combinando información a diversas escalas anatómicas del cerebro y diferentes modalidades de imágenes se puede mejorar la precisión de la clasificación. De este modo, se han utilizado volúmenes regionales y globales (multiscale) de Materia Gris (GM), volúmenes regionales de Materia Blanca (WM), Coeficientes de asimetría e información de MRI T1 cuantitativa (multivariate). Nuestras precisiones cuando comparamos CTL vs EA y CTL vs MCI usando bases de datos públicas (ADNI) son comparables a los resultados de la literatura: 88.3% y 81.8% respectivamente. En este proyecto también estudiamos una base de datos más pequeña de pacientes con MCI del Lausanne University Hospital. Prestamos especial atención al estudio de los pasos de pre-procesado: normalización por Volumen InterCraneal y corrección de edad. Los resultados obtenidos muestran que, para nuestro grupo reducido de pacientes, se obtienen precisiones mejores cuando se combinan diferentes tipos de datos (multiscale y multivariate) que cuando solamente se usan los clásicos volúmenes regionales de GM. Además, el nuevo método propuesto de corrección de edad basado en regiones presenta resultados esperanzadores cuando se aplica previo a ambas clasificaciones CTL vsMCI y CTL vs EA.[CATALÀ] La Malaltia d'Alzheimer (MA) és la forma més comuna de demència i ha esdevingut un problema socioeconòmic creixent. A més, es preveu que l'impacte de la malaltia serà encara més gran d'aquí a uns anys a causa del progressiu envelliment de la població mundial i al creixement de l'esperança de vida. És per aquestes raons que en els últims anys s'ha centrat l'atenció en tècniques computaritzades per la diagnosi que estan dirigides a l'estudi quantitatiu d'imatges de ressonància magnètica (MRI) del cervell de pacients que es troben en una etapa primerenca de la malaltia. Un diagnòstic precoç podria millorar l'efectivitat dels futurs tractaments de curació o modificació del curs natural de la malaltia. Un exemple d'etapa primerenca de MA és el Deteriorament Cognitiu Lleuger (Mild Cognitive Impariment o MCI), ja que el 50 % dels pacients que pateixen aquesta patologia desenvolupen MA en tres o quatre anys. En aquest estudi, fem servir Support Vector Machines per classificar subjectes de tres grups diferents: MA, MCI i subjectes sans de control (CTL). El nostre objectiu és estudiar si combinant informació a diverses escales anatòmiques del cervell i diferents modalitats d'imatge es pot millorar la precisió de la classificació. D'aquesta manera, s'han utilitzat volums regionals i globals (multiscale) de Matèria Gris (GM), volums regionals de Matèria Blanca (WM), Coeficients d'asimetria i informació de MRI T1 quantitativa (Multivariate). Les nostres precisions quan comparem CTL vs MA i CTL vs MCI amb bases de dades públiques (ADNI) són comparables als resultats de la literatura: 88.3 % i 81.8 % respectivament. En aquest projecte també estudiem una base de dades més petita de pacients amb MCI del Lausanne University Hospital. Prestem especial atenció a l'estudi dels passos de preprocessament: normalització per Volum intercranial i correcció d'edat. Els resultats obtinguts mostren que, pel nostre grup reduït de pacients, s'obtenen millors precisions quan es combinen diferents tipus de dades (multiscale iMultivariate) que quan només s'usen els clàssics volums regionals de GM. A més, el nou mètode proposat de correcció d'edat basat en regions presenta resultats esperançadors quan s'aplica previ a la classificació CTL vsMCI i CTL vs MA