Evaluation of Cerebral Lateral Ventricular Enlargement Derived from Magnetic Resonance Imaging: A Candidate Biomarker of Alzheimer Disease Progression in Vivo

Alzheimer disease (AD) is the most common form of dementia and has grievous mortality rates. Measuring brain volumes from structural magnetic resonance images (MRI) may be useful for illuminating disease progression. The goal of this thesis was to (1) help refine a novel technique used to segment the lateral cerebral ventricles from MRI, (2) validate this tool, and determine group-wise differences between normal elderly controls (NEC) and subjects with mild cognitive impairment (MCI) and AD and (3) determine the number of subjects necessary to detect a 20 percent change from the natural history of ventricular enlargement with respect to genotype. Three dimensional Ti-weighted MRI and cognitive measures were acquired from 504 subjects (NEC n = 152, MCI n = 247 and AD n = 105) participating in the multi-centre Alzheimer\u27s Disease Neuroimaging Initiative. Cerebral ventricular volume was quantified at baseline and after six months. For secondary analyses, all groups were dichotomized for Apolipoprotein E genotype based on the presence of an e4 polymorphism. The AD group had greater ventricular enlargement compared to both subjects with MCI (P = 0.0004) and NEC (P \u3c 0.0001), and subjects with MCI had a greater rate of ventricular enlargement compared to NEC (P =0.0001). MCI subjects that progressed to clinical AD after six months had greater ventricular enlargement than stable MCI subjects (P = 0.0270). Ventricular enlargement was different between apolipoprotein E genotypes within the AD group (P = 0.010). The number of subjects required to demonstrate a 20% change in ventricular enlargement (AD: N=342, MCI: N=1180) was substantially lower than that required to demonstrate a 20% change in cognitive scores (MMSE) (AD: N=7056, MCI: N=7712). Therefore, ventricular enlargement represents a feasible short-term marker of disease progression in subjects with MCI and subjects with AD for multi-centre studie

An Intelligent Hybrid Optimization with Deep Learning model-based Schizophrenia Identification from Structural MRI

One of the fatal diseases that claim women while they are pregnant or nursing is schizophrenia. Despite several developments and symptoms, it can be challenging to discern between benign and malignant conditions. The main and most popular imaging method to predict Schizophrenia is MR Images. Furthermore, a few earlier models had a definite accuracy when diagnosing the condition. Stable MRI criteria must also be implemented immediately. Compared to other imaging technologies, the MRI imaging method is the simplest, safest, and most common for predicting Schizophrenia. The following factors are mostly involved in the subprocess for the initial MRI image. Before calculating the length between the sample point and the cluster center, the initial cluster center of the sample is identified. Classification is done according to how far the sample point is from the cluster center. The picture is then generated once the new cluster center has been derived using the classification history and verified to match the cluster convergence condition. A grey wolf optimization-based convolutional neural network approach is offered to get beyond the limitations and find schizophrenia, whether its hazardous or not. Many MRI images or datasets are analyzed in a short time, and the results show a more accurate or higher rate of schizophrenia recognition

Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries

Novel Deep Learning Models for Medical Imaging Analysis

abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

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

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus