DIAGNOSTICS OF DEMENTIA FROM STRUCTURAL AND FUNCTIONAL MARKERS OF BRAIN ATROPHY WITH MACHINE LEARNING

Dementia is a condition in which higher mental functions are disrupted. It currently affects an estimated 57 million people throughout the world. A dementia diagnosis is difficult since neither anatomical indicators nor functional testing is currently sufficiently sensitive or specific. There remains a long list of outstanding issues that must be addressed. First, multimodal diagnosis has yet to be introduced into the early stages of dementia screening. Second, there is no accurate instrument for predicting the progression of pre-dementia. Third, non-invasive testing cannot be used to provide differential diagnoses. By creating ML models of normal and accelerated brain aging, we intend to better understand brain development. The combined analysis of distinct imaging and functional modalities will improve diagnostics of accelerated decline with advanced data science techniques, which is the main objective of our study. Hypothetically, an association between brain structural changes and cognitive performance differs between normal and accelerated aging. We propose using brain MRI scans to estimate the cognitive status of the cognitively preserved examinee and develop a structure-function model with machine learning (ML). Accelerated ageing is suspected when a scanned individual’s findings do not align with the usual paradigm. We calculate the deviation from the model of normal ageing (DMNA) as the error of cognitive score prediction. Then the obtained data may be compared with the results of conducted cognitive tests. The greater the difference between the expected and observed values, the greater the risk of dementia. DMNA can discern between cognitively normal and mild cognitive impairment (MCI) patients. The model was proven to perform well in the MCI-versus-Alzheimer’s disease (AD) categorization. DMNA is a potential diagnostic marker of dementia and its types

ACTIVATED CARBON NANOFIBERS FROM RENEWABLE (LIGNIN) AND WASTE RESOURCES (RECYCLED PET) AND THEIR ADSORPTION CAPACITY OF REFRACTORY SULFUR COMPOUNDS FROM FOSSIL FUELS

Dementia is a condition in which higher mental functions are disrupted. It currently affects an estimated 57 million people throughout the world. Dementia diagnosis is difficult since neither anatomical indicator nor functional testing are currently sufficiently sensitive or specific. There remains a long list of outstanding issues that must be addressed. First, multimodal diagnosis has yet to be introduced into the early stages of dementia screening. Second, there is no accurate instrument for predicting the progression of pre-dementia. Third, non-invasive testing cannot be used to provide differential diagnoses. By creating ML models of normal and accelerated brain aging, we intend to better understand brain development. The combined analysis of distinct imaging and functional modalities will improve diagnostics of accelerated decline with advanced data science techniques, which is the main objective of our study. Hypothetically, an association between brain structural changes and cognitive performance differs between normal and accelerated aging. We propose using brain MRI scans to estimate the cognitive status of the cognitively preserved examinee and develop a structure-function model with machine learning (ML). Accelerated aging is suspected when a scanned individual’s findings do not align with the usual paradigm. We calculate the deviation from the model of normal aging (DMNA) as the error of cognitive score prediction. Then the obtained data may be compared with the results of conducted cognitive tests. The greater the difference between the expected and observed values, the greater the risk of dementia. DMNA can discern between cognitively normal and mild cognitive impairment (MCI) patients. The model was proven to perform well in the MCI-versus-Alzheimer’s disease (AD) categorization. DMNA is a potential diagnostic marker of dementia and its types

Using Unsupervised Learning Methods to Analyse Magnetic Resonance Imaging (MRI) Scans for the Detection of Alzheimer’s Disease

Background: Alzheimer’s disease (AD) is the most common cause of dementia, characterised by behavioural and cognitive impairment. The manual diagnosis of AD by doctors is time-consuming and can be ineffective, so machine learning methods are increasingly being proposed to diagnose AD in many recent studies. Most research developing machine learning algorithms to diagnose AD use supervised learning to classify magnetic resonance imaging (MRI) scans. However, supervised learning requires a considerable volume of labelled data and MRI scans are difficult to label. The aim of this thesis was therefore to use unsupervised learning methods to differentiate between MRI scans from people who were cognitively normal (CN), people with mild cognitive impairment (MCI), and people with AD. Objectives: This study applied a statistical method and unsupervised learning methods to discriminate scans from (1) people with CN and with AD; (2) people with stable mild cognitive impairment (sMCI) and with progressive mild cognitive impairment (pMCI); (3) people with CN and with pMCI, using a limited number of labelled structural MRI scans. Methods: Two-sample t-tests were used to detect the regions of interest (ROIs) between each of the two groups (CN vs. AD; sMCI vs. pMCI; CN vs. pMCI), and then an unsupervised learning neural network was employed to extract features from the regions. Finally, a clustering algorithm was implemented to discriminate between each of the two groups based on the extracted features. The approach was tested on baseline brain structural MRI scans from 715 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), of which 231 were CN, 198 had AD, 152 had sMCI, and 134 were pMCI. The results were evaluated by calculating the overall accuracy, the sensitivity, specificity, and positive and negative predictive values. Results: The abnormal regions around the lower parts of the limbic system were indicated as AD-relevant regions based on the two-sample t-test (p<0.001), and the proposed method yielded an overall accuracy of 0.842 for discriminating between CN and AD, an overall accuracy of 0.672 for discriminating between sMCI and pMCI, and an overall accuracy of 0.776 for discriminating between CN and pMCI. Conclusion: The study combined statistical and unsupervised learning methods to identify scans of people with different stages of AD. This method can detect AD-relevant regions and could be used to accurately diagnose stages of AD; it has the advantage that it does not require large amounts of labelled MRI scans. The performances of the three discriminations were all comparable to those of previous state-of-the-art studies. The research in this thesis could be implemented in the future to help in the automatic diagnosis of AD and provide a basis for diagnosing sMCI and pMCI

Automatic Pancreas Segmentation and 3D Reconstruction for Morphological Feature Extraction in Medical Image Analysis

The development of highly accurate, quantitative automatic medical image segmentation techniques, in comparison to manual techniques, remains a constant challenge for medical image analysis. In particular, segmenting the pancreas from an abdominal scan presents additional difficulties: this particular organ has very high anatomical variability, and a full inspection is problematic due to the location of the pancreas behind the stomach. Therefore, accurate, automatic pancreas segmentation can consequently yield quantitative morphological measures such as volume and curvature, supporting biomedical research to establish the severity and progression of a condition, such as type 2 diabetes mellitus. Furthermore, it can also guide subject stratification after diagnosis or before clinical trials, and help shed additional light on detecting early signs of pancreatic cancer. This PhD thesis delivers a novel approach for automatic, accurate quantitative pancreas segmentation in mostly but not exclusively Magnetic Resonance Imaging (MRI), by harnessing the advantages of machine learning and classical image processing in computer vision. The proposed approach is evaluated on two MRI datasets containing 216 and 132 image volumes, achieving a mean Dice similarity coefficient (DSC) of 84:1 4:6% and 85:7 2:3% respectively. In order to demonstrate the universality of the approach, a dataset containing 82 Computer Tomography (CT) image volumes is also evaluated and achieves mean DSC of 83:1 5:3%. The proposed approach delivers a contribution to computer science (computer vision) in medical image analysis, reporting better quantitative pancreas segmentation results in comparison to other state-of-the-art techniques, and also captures detailed pancreas boundaries as verified by two independent experts in radiology and radiography. The contributions’ impact can support the usage of computational methods in biomedical research with a clinical translation; for example, the pancreas volume provides a prognostic biomarker about the severity of type 2 diabetes mellitus. Furthermore, a generalisation of the proposed segmentation approach successfully extends to other anatomical structures, including the kidneys, liver and iliopsoas muscles using different MRI sequences. Thus, the proposed approach can incorporate into the development of a computational tool to support radiological interpretations of MRI scans obtained using different sequences by providing a “second opinion”, help reduce possible misdiagnosis, and consequently, provide enhanced guidance towards targeted treatment planning

Employing visual analytics to aid the design of white matter hyperintensity classifiers

Accurate segmentation of brain white matter hyperintensities (WMHs) is important for prognosis and disease monitoring. To this end, classifiers are often trained – usually, using T1 and FLAIR weighted MR images. Incorporating additional features, derived from diffusion weighted MRI, could improve classification. However, the multitude of diffusion-derived features requires selecting the most adequate. For this, automated feature selection is commonly employed, which can often be sub-optimal. In this work, we propose a different approach, introducing a semi-automated pipeline to select interactively features for WMH classification. The advantage of this solution is the integration of the knowledge and skills of experts in the process. In our pipeline, a Visual Analytics (VA) system is employed, to enable user-driven feature selection. The resulting features are T1, FLAIR, Mean Diffusivity (MD), and Radial Diffusivity (RD) – and secondarily, Cs and Fractional Anisotropy (FA). The next step in the pipeline is to train a classifier with these features, and compare its results to a similar classifier, used in previous work with automated feature selection. Finally, VA is employed again, to analyze and understand the classifier performance and results

Employing visual analytics to aid the design of white matter hyperintensity classifiers

Accurate segmentation of brain white matter hyperintensities (WMHs) is important for prognosis and disease monitoring. To this end,classifiers are often trained – usually,using T1 and FLAIR weighted MR images. Incorporating additional features,derived from diffusion weighted MRI,could improve classification. However,the multitude of diffusion-derived features requires selecting the most adequate. For this,automated feature selection is commonly employed,which can often be sub-optimal. In this work,we propose a different approach,introducing a semi-automated pipeline to select interactively features for WMH classification. The advantage of this solution is the integration of the knowledge and skills of experts in the process. In our pipeline,a Visual Analytics (VA) system is employed,to enable user-driven feature selection. The resulting features are T1,FLAIR,Mean Diffusivity (MD),and Radial Diffusivity (RD) – and secondarily,CS and Fractional Anisotropy (FA). The next step in the pipeline is to train a classifier with these features,and compare its results to a similar classifier,used in previous work with automated feature selection. Finally,VA is employed again,to analyze and understand the classifier performance and results