Detection and classification of neurodegenerative diseases: a spatially informed bayesian deep learning approach

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesNeurodegenerative diseases comprise a group of chronic and irreversible conditions characterized by the progressive degeneration of the structure and function of the central nervous system. The detection and classification of patients according to the underlying disease are crucial for developing oriented treatments and enriching prognosis. In this context, Magnetic resonance imaging (MRI) data can provide meaningful insights into neurodegeneration by detecting the physiological manifestations in the brain caused by the disease processes. One field of extensive clinical use of MRI is the accurate and automated classification of neurodegenerative disorders. Most studies distinguish patients from healthy subjects or stages within the same disease. Such distinction does not mirror clinical practice, as a patient may not show all symptoms, especially if the disease is in an early stage, or show, due to comorbidities, other symptoms as well. Likewise, automated classifiers are partly suited for medical diagnosis since they cannot produce probabilistic predictions nor account for uncertainty. Also, existent studies ignore the spatial heterogeneity of the brain alterations caused by neurodegenerative processes. The spatial configuration of the neuronal loss is a characteristic hallmark for each disorder. To fill these gaps, this thesis aims to develop a classification technique that incorporates uncertainty and spatial information for distinguishing four neurodegenerative diseases, Alzheimer’s disease, Mild cognitive impairment, Parkinson’s disease and Multiple Sclerosis, and healthy subjects. This technique will produce automated, contingent, and accurate predictions to support clinical diagnosis. To quantify prediction uncertainty and improve classification accuracy, this study introduces a Bayesian neural network with a spatially informed input. A convolutional neural network (CNN) is developed to identify a neurodegenerative condition based on T1weighted MRI scans from patients and healthy controls. Bayesian inference is incorporated into the CNN to measure uncertainty and produce probabilistic predictions. Also, a spatially informed MRI scan is added to the CNN to improve feature detection and classification accuracy. The Spatially informed Bayesian Neural Network (SBNN) proposed in this work demonstrates that classification accuracy can be increased up to 25% by including the spatially informed MRI scan. Furthermore, the SBNN provides robust probabilistic diagnosis that resembles clinical decision-making and accounts for atypical, numerous, and early presentations of neurodegenerative disorders

Brain tumor classification in magnetic resonance imaging images using convolutional neural network

Deep learning (DL) is a subfield of artificial intelligence (AI) used in several sectors, such as cybersecurity, finance, marketing, automated vehicles, and medicine. Due to the advancement of computer performance, DL has become very successful. In recent years, it has processed large amounts of data, and achieved good results, especially in image analysis such as segmentation and classification. Manual evaluation of tumors, based on medical images, requires expensive human labor and can easily lead to misdiagnosis of tumors. Researchers are interested in using DL algorithms for automatic tumor diagnosis. convolutional neural network (CNN) is one such algorithm. It is suitable for medical image classification tasks. In this paper, we will focus on the development of four sequential CNN models to classify brain tumors in magnetic resonance imaging (MRI) images. We followed two steps, the first being data preprocessing and the second being automatic classification of preprocessed images using CNN. The experiments were conducted on a dataset of 3,000 MRI images, divided into two classes: tumor and normal. We obtained a good accuracy of 98,27%, which outperforms other existing models

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

An Information Theoretic Approach For Feature Selection And Segmentation In Posterior Fossa Tumors

Posterior Fossa (PF) is a type of brain tumor located in or near brain stem and cerebellum. About 55% - 70 % pediatric brain tumors arise in the posterior fossa, compared with only 15% - 20% of adult tumors. For segmenting PF tumors we should have features to study the characteristics of tumors. In literature, different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) have been exploited for measuring randomness associated with brain and tumor tissues structures, and the varying appearance of tissues in magnetic resonance images (MRI). For selecting best features techniques such as neural network and boosting methods have been exploited. However, neural network cannot descirbe about the properties of texture features. We explore methods such as information theroetic methods which can perform feature selection based on properties of texture features. The primary contribution of this dissertation is investigating efficacy of different image features such as intensity, fractal texture, and level - set shape in segmentation of PF tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques respectively to discriminate tumor regions from normal tissue in multimodal brain MRI. Our research suggest that Kullback - Leibler Divergence (KLD) measure for feature ranking and selection and Expectation Maximization (EM) algorithm for feature fusion and tumor segmentation offer the best performance for the patient data in this study. To improve segmentation accuracy, we need to consider abnormalities such as cyst, edema and necrosis which surround tumors. In this work, we exploit features which describe properties of cyst and technique which can be used to segment it. To achieve this goal, we extend the two class KLD techniques to multiclass feature selection techniques, so that we can effectively select features for tumor, cyst and non tumor tissues. We compute segemntation accuracy by computing number of pixels segemented to total number of pixels for the best features. For automated process we integrate the inhomoheneity correction, feature selection using KLD and segmentation in an integrated EM framework. To validate results we have used similarity coefficients for computing the robustness of segmented tumor and cyst

Proceedings of the MICCAI Challenge on Multimodal Brain Tumor Image Segmentation (BRATS) 2013

International audienceBecause of their unpredictable appearance and shape, segmenting brain tumors from multi-modal imaging data is one of the most challenging tasks in medical image analysis. Although many different segmentation strategies have been proposed in the literature, it is hard to compare existing methods because the validation datasets that are used differ widely in terms of input data (structural MR contrasts; perfusion or diffusion data; ...), the type of lesion (primary or secondary tumors; solid or infiltratively growing), and the state of the disease (pre- or post-treatment). In order to gauge the current state-of-the-art in automated brain tumor segmentation and compare between different methods, we are organizing a Multimodal Brain Tumor Image Segmentation (BRATS) challenge that is held in conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013) on September 22nd, 2013 in Nagoya, Japan

Imaging of cognitive outcomes in patients with autoimmune encephalitis

Die Autoimmunenzephalitis ist eine kürzlich beschriebene entzündliche Erkrankung des zentralen Nervensystems, die Gedächtnisdefizite, Psychosen, oder epileptische Anfälle hervorrufen kann. Derzeit ist hingegen noch nicht ausreichend verstanden, welche pathologischen Veränderungen zu den kognitiven Defiziten führen und welche neuropsychologischen und bildgebenden Langzeitoutcomes zu erwarten sind. Anhand von strukturellen und funktionellen Bildgebungsanalysen zeigt diese Dissertation, dass kognitive Defizite auch nach der akuten Phase der Autoimmunenzephalitis fortbestehen können. Bei der LGI1-Enzephalitis gehen Gedächtnisdefizite mit fokalen strukturellen Läsionen im Hippocampus einher. Durch eine funktionelle Störung der Resting-State-Konnektivität des Default-Mode- und Salienznetzwerkes beeinträchtigen diese Hippocampusläsionen auch Hirnregionen außerhalb des limbischen Systems. Bei Patient:innen mit NMDA-Rezeptor-Enzephalitis finden sich in der longitudinalen neuropsychologischen Untersuchung trotz guter allgemeiner Genesung auch noch mehrere Jahre nach der Akutphase persistierende Defizite des Gedächtnisses und exekutiver Funktionen. Zuletzt zeigt eine transdiagnostische Analyse, dass der anteriore Hippocampus eine erhöhte Vulnerabilität gegenüber immunvermittelten pathologischen Prozessen aufweist. Diese Ergebnisse legen nahe, dass kognitive Symptome auch noch nach der Entlassung aus der stationären Behandlung fortbestehen können. Sowohl umschriebene strukturelle Hippocampusläsionen als auch Veränderungen in makroskopischen funktionellen Hirnnetzwerken tragen zur pathophysiologischen Erklärung dieser Symptome bei. Zudem erlauben diese Ergebnisse einen Einblick in neuroplastische Veränderungen des Gehirns und haben weitreichende Implikationen für die Langzeitversorgung und das Design zukünftiger klinischer Studien.Autoimmune encephalitis is a recently described inflammatory disease of the central nervous system that can cause memory deficits, psychosis, or seizures. The trajectory of cognitive dysfunction and the underlying long-term imaging correlates are, however, not yet fully understood. By using advanced structural and functional neuroimaging, this thesis shows that cognitive deficits persist beyond the acute phase. In LGI1 encephalitis, MRI postprocessing revealed that memory deficits are related to focal structural hippocampal lesions. These hippocampal lesions propagate to brain areas outside the limbic system through aberrant resting-state connectivity of the default mode network (DMN) and the salience network. In NMDA receptor encephalitis, a longitudinal analysis of neuropsychological data describes persistent cognitive deficits, especially in the memory and executive domains, despite good physical recovery several years after the acute disease. Lastly, a transdiagnostic analysis reveals that the anterior hippocampus is particularly vulnerable to immune-mediated damage. In conclusion, these results demonstrate that cognitive symptoms in autoimmune encephalitis can persist beyond discharge from neurological care. Both discrete structural hippocampal damage and changes in macroscopic functional networks shed light on the pathophysiological basis of these symptoms. These findings help to explain how the brain responds to pathological damage and have substantial implications for long-term patient care and the design of future clinical studies

Computational Modeling for Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, and Grading

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology (DP) images. Due to uncontrolled growth and infiltration into the surrounding tissues, the tumor regions appear with a significant texture variation in the static MRI volume and also in the longitudinal imaging study. Consequently, this study developed computational models using novel texture features to segment abnormal brain tissues (tumor, and stroke lesions), tracking the change of tumor volume in longitudinal images, and tumor grading in MR images. Manual delineation and analysis of these abnormal tissues in large scale is tedious, error-prone, and often suffers from inter-observer variability. Therefore, efficient computational models for robust segmentation of different abnormal tissues is required to support the diagnosis and analysis processes. In this study, brain tissues are characterized with novel computational modeling of multi-fractal texture features for multi-class brain tumor tissue segmentation (BTS) and extend the method for ischemic stroke lesions in MRI. The robustness of the proposed segmentation methods is evaluated using a huge amount of private and public domain clinical data that offers competitive performance when compared with that of the state-of-the-art methods. Further, I analyze the dynamic texture behavior of tumor volume in longitudinal imaging and develop post-processing frame-work using three-dimensional (3D) texture features. These post-processing methods are shown to reduce the false positives in the BTS results and improve the overall segmentation result in longitudinal imaging. Furthermore, using this improved segmentation results the change of tumor volume has been quantified in three types such as stable, progress, and shrinkage as observed by the volumetric changes of different tumor tissues in longitudinal images. This study also investigates a novel non-invasive glioma grading, for the first time in literature, that uses structural MRI only. Such non-invasive glioma grading may be useful before an invasive biopsy is recommended. This study further developed an automatic glioma grading scheme using the invasive cell nuclei morphology in DP images for cross-validation with the same patients. In summary, the texture-based computational models proposed in this study are expected to facilitate the clinical management of patients with the brain tumors and strokes by automating large scale imaging data analysis, reducing human error, inter-observer variability, and producing repeatable brain tumor quantitation and grading