Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

Coronal slice segmentation using a watershed method for early identification of people with Alzheimer's

One physical sign of a person who has Alzheimer's is the diminution of the area of the hippocampus and ventricles. A good quality magnetic resonance imaging (MRI) will provide a high-quality image so that the doctor will quickly analyze the abnormalities of the hippocampus and ventricle area. However, for low-quality MRI, this is difficult to do. This condition will be a significant problem for some regions in developing countries including Indonesia, where many hospitals have only low-quality MRI, and many hospitals do not have them at all. The primary purpose of this research is to develop simple tools to analyze morphological characteristics in Alzheimer's patients. In this paper, we focus only on coronal slice analysis. We will use watershed method segmentation, because of this method able to segment the boundaries automatically, so that parts of the hippocampus and ventricles can be identified in an MRI image. Analysis of morphological characteristics is also classified by age and gender. Then by referring to the value of the clinical dementia rating (CDR), the process of identifying between images with Alzheimer's disease (AD) and healthy models is done based on the morphological analysis that has been done. The results show this method has a better performance compared to the previously work

A Biomarker for Alzheimer’s Disease Based on Patterns of Regional Brain Atrophy

Introduction: It has been shown that Alzheimer’s disease (AD) is accompanied by marked structural brain changes that can be detected several years before clinical diagnosis via structural magnetic resonance (MR) imaging. In this study, we developed a structural MR-based biomarker for in vivo detection of AD using a supervised machine learning approach. Based on an individual’s pattern of brain atrophy a continuous AD score is assigned which measures the similarity with brain atrophy patterns seen in clinical cases of AD. Methods: The underlying statistical model was trained with MR scans of patients and healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1 screening). Validation was performed within ADNI-1 and in an independent patient sample from the Open Access Series of Imaging Studies (OASIS-1). In addition, our analyses included data from a large general population sample of the Study of Health in Pomerania (SHIP-Trend). Results: Based on the proposed AD score we were able to differentiate patients from healthy controls in ADNI-1 and OASIS-1 with an accuracy of 89% (AUC = 95%) and 87% (AUC = 93%), respectively. Moreover, we found the AD score to be significantly associated with cognitive functioning as assessed by the Mini-Mental State Examination in the OASIS-1 sample after correcting for diagnosis, age, sex, age·sex, and total intracranial volume (Cohen’s f2 = 0.13). Additional analyses showed that the prediction accuracy of AD status based on both the AD score and the MMSE score is significantly higher than when using just one of them. In SHIP-Trend we found the AD score to be weakly but significantly associated with a test of verbal memory consisting of an immediate and a delayed word list recall (again after correcting for age, sex, age·sex, and total intracranial volume, Cohen’s f2 = 0.009). This association was mainly driven by the immediate recall performance. Discussion: In summary, our proposed biomarker well differentiated between patients and healthy controls in an independent test sample. It was associated with measures of cognitive functioning both in a patient sample and a general population sample. Our approach might be useful for defining robust MR-based biomarkers for other neurodegenerative diseases, too

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

An Ensemble-of-Classifiers Based Approach for Early Diagnosis of Alzheimer’s Disease: Classification Using Structural Features of Brain Images

Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer’s disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity

Doctor of Philosophy

dissertationAn important aspect of medical research is the understanding of anatomy and its relation to function in the human body. For instance, identifying changes in the brain associated with cognitive decline helps in understanding the process of aging and age-related neurological disorders. The field of computational anatomy provides a rich mathematical setting for statistical analysis of complex geometrical structures seen in 3D medical images. At its core, computational anatomy is based on the representation of anatomical shape and its variability as elements of nonflat manifold of diffeomorphisms with an associated Riemannian structure. Although such manifolds effectively represent natural biological variability, intrinsic methods of statistical analysis within these spaces remain deficient at large. This dissertation contributes two critical missing pieces for statistics in diffeomorphisms: (1) multivariate regression models for cross-sectional study of shapes, and (2) generalization of classical Euclidean, mixed-effects models to manifolds for longitudinal studies. These models are based on the principle that statistics on manifold-valued information must respect the intrinsic geometry of that space. The multivariate regression methods provide statistical descriptors of the relationships of anatomy with clinical indicators. The novel theory of hierarchical geodesic models (HGMs) is developed as a natural generalization of hierarchical linear models (HLMs) to describe longitudinal data on curved manifolds. Using a hierarchy of geodesics, the HGMs address the challenge of modeling the shape-data with unbalanced designs typically arising as a result of follow-up medical studies. More generally, this research establishes a mathematical foundation to study dynamics of changes in anatomy and the associated clinical progression with time. This dissertation also provides efficient algorithms that utilize state-of-the-art high performance computing architectures to solve models on large-scale, longitudinal imaging data. These manifold-based methods are applied to predictive modeling of neurological disorders such as Alzheimer's disease. Overall, this dissertation enables clinicians and researchers to better utilize the structural information available in medical images

Fractal tool; Calculating 3D fractal dimension of brain regions for a Dementia classification problem

Η άνοια είναι ένα σύνδρομο που είναι κοινό στους ηλικιωμένους και ο ρυθμός εμφάνισής του αυξάνεται. Η πλειονότητα των μελετών επικεντρώνεται στην εύρεση βιοδεικτών για διάγνωση, ενώ η πρόληψη και η παρακολούθηση της ανάπτυξης είναι ακόμη ένα αδύνατο έργο. Σήμερα, η έρευνα για την άνοια περιορίζεται στη νευροαπεικόνιση, καθώς είναι μια μη επεμβατική τεχνολογία. Υπάρχει μια πληθώρα νευροαπεικονιστικών εργαλείων τα οποία βελτιστοποιούν την απεικόνιση μιας εισαγόμενης εικόνας μέσω της επεξεργασίας εικόνας ή ακόμη και συνεισφέρουν στη λήψη ιατρικών αποφάσεων μέσω της ανάλυσης της εικόνας. Ωστόσο, η ατροφία του εγκεφάλου στην άνοια δεν έχει ακόμη χαρακτηριστεί σωστά. Η νευροαπεικόνιση στοχεύει κυρίως στην παρακολούθηση της μείωσης του όγκου του εγκεφάλου και λιγότερο σε άλλες δομικές υφές. Τέτοια χαρακτηριστικά είναι η μορφοκλασματική διάσταση και η ύπαρξη κενών στις εγκαφαλικές δομές (lacunarity). Μερικά εργαλεία νευροαπεικόνισης υπολογίζουν τη διάσταση του φράκταλ και του lacunarity για ολόκληρο τον όγκο του εγκεφάλου. Ωστόσο, η λειτουργικότητά τους δεν περιλαμβάνει αυτοματοποιημένη εκτίμηση πολλαπλών εικόνων και, συνεπώς, δημιουργία συνόλων δεδομένων (datasets). Υπολογίζουν απλώς αυτά τα χαρακτηριστικά για συγκεκριμένες δομές, ενώ δεν εκτελούν τα απαραίτητα βήματα επεξεργασίας εικόνας. Δεδομένου ότι τα περισσότερα από αυτά τα εργαλεία εξειδικεύονται σε συγκεκριμένες εργασίες, δεν υπάρχει μια ολιστική μέθοδος που εισάγει πολλαπλά δεδομένα απεικόνισης και εξάγει μετρήσεις για τη μορφοκλασματική διάσταση και το lacunarity. Αυτή η μελέτη παρουσιάζει ένα εργαλείο γενικού σκοπού για την αυτοματοποιημένη επεξεργασία εικόνας, την τμηματοποίηση εικόνας, την εκτίμηση της μορφοκλασματική διάσταση, του lacunarity και άλλων υφών που προέρχονται από τον υπολογισμό της μορφοκλασματικής διάστασης. Τα εξαγόμενα αρχεία είναι σύνολα δεδομένων με τέτοιες μετρήσεις. Έπειτα γίνεται μια ταξινόμηση υγειών και ατόμων με άνοια η οποία επιβεβαιώνει τη χρησιμότητα του λογισμικού. Παρόλο που υπήρχαν περιορισμοί στην απόκτηση δεδομένων, πραγματοποιήθηκε αποτελεσματική ταξινόμηση με μηχανές διανυσματικής υποστήριξης. Για αρκετές περιοχές του εγκεφάλου, η ακρίβεια Fbeta score κυμαινόταν μεταξύ 95% και 100% υπερισχύοντας όλων των άλλων μεθόδων. Ωστόσο, η διάγνωση της άνοιας απαιτεί ένα μοντέλο που διαχωρίζει αποτελεσματικά τις περιοχές του εγκεφάλου για όλες τις κλάσεις. Σε αυτή τη διατριβή, εκπαιδεύτηκαν δύο τέτοια μοντέλα, ένα κάθε ομάδα. Παρ 'όλα αυτά, τα τελικά αποτελέσματα αναδεικνύουν την αναγκαιότητα των μορφοκλασματικών ιδιοτήτων ως εργαλείο για τη ταξινόμηση των σταδίων της άνοιας και τη παρακολούθηση της ανάπτυξης της άνοιας. Επίσης, η χρήση του λογισμικού μπορεί να επεκταθεί σε οποιοδήποτε πρόβλημα δομικής νευροαπεικόνισης όπως η ανίχνευση καρκίνουDementia is a syndrome that is common amongst the elder adults and its occurrence rate is on the rise. The majority of the studies are focused on finding biomarkers for diagnosis, while prevention and monitoring of the development is yet an impossible task. Nowadays, research on dementia is limited to neuroimaging as it is a non-invasive technology. There is a plethora of neuroimaging tools which optimize the virtualization of an imported image through image processing or even contribute medical decision making through image analysis. Still, brain atrophy in Dementia is yet to be characterized properly. Neuroimaging mainly aims on volume decline of brain volume and less on other structural textures. Such features are fractal dimension and lacunarity. Some neuroimaging tools calculate fractal dimension and lacunarity for whole brain volume. However, their functionality does not include automated estimation of multiple images and thus creation of datasets. They just compute these features for given structures while not performing the necessary image processing steps. As most of these tools are specialized in specific tasks, there is not a holistic method that inputs multiple imaging data and exports measurements for fractal dimension, lacunarity. This study presents a general purpose tool for automated image processing, image segmentation, estimation of fractal dimension, lacunarity and other textures that are derived from the calculation of fractal dimension. The exported files are datasets with such measurements. Finally, a classification between healthy and dementia subjects underlines the utility of the software. Even though there were limitations to data acquirement, efficient classification with SVM models has been performed. For several brain regions, the ranging Fbeta score accuracy was 97% - 100 % outperforming all other methods. However, diagnosis of Dementia requires a unique prior model which efficiently segment brain regions for any given class. In this thesis, two models were trained, one for each group. Nevertheless, the final results reveal the necessity of fractal properties as a tool for Dementia classification and monitoring of the Dementia development. Also, the utility of the software can be extended to any structural neuroimaging problem such as detection of cancer

Morphometric data fusion for early detection of alzheimer’s disease

Abstract. We present a morphometry method which uses brain models generated using Nonnegative Matrix Factorization (NMF) characterized by signatures calculated from perceptual features such as intensities, edges and orientations, of some regions obtained by comparing the models. Two different measures are used to calculate volume-models distances in the regions of interest. The discerning power of these distances is tested by using them as features for a Support Vector Machine classifier. This work shows the usefulness of both measures as metrics in medical image applications when they are used in binary classification tasks. Our methodology was tested with two experimental groups extracted from a public brain MR dataset (OASIS), the classification between healthy subjects and patients with mild AD reveals an equal error rate (EER) measure which is better than previous approaches tested on the same dataset (0.1 in the former and 0.2 in the latter). When detecting very mild AD, our results (near to 75% of sensitivity and specificity) are comparable to the results with those approaches.Presentamos un m´etodo de morfometr´ı que usa modelos de cerebro que se generan usando factorizaci´on de matrices no-negativas (NMF por su nombre en ingl´es) y se caracterizan por firmas calculadas de rasgos perceptules como las intensidades, bordes y orientaciones de algunas regiones del cerebro obtenidas de la comparaci´on entre modelos. Dos medidas, la divergencia de Kullback-Leibler y la “Earth Mover’s Distance”, son usadas para calcular la distancia entre vol´umenes y modelos en las regiones de inter´es. Probamos el poder discriminante de estas distancias us´andolas para construir los vectores de caracter´ısticas para una m´aquina de soporte vectorial. Este trabajo muestra la utilidad de ambas medidas en tareas de clasificaci´on binaria. Nuestra metodolog´ıa fue probada con dos grupos experimentales extra´ıdos de la base de datos OASIS, la clasificaci´on entre sujetos sanos y pacientes con Alzheimer leve revela un EER que mejora los resultados obtenidos por trabajos publicados previamente con los mismos grupos experimentales. Cuando se trata de detectar Alzheimer muy leve, los resultados (cercanos a 75% de sensibilidad y especificidad) son comparables con los resultados obtenidos en dichas publicaciones.Maestrí

Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's disease: A Methodological Review

Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging especially in Mild Cognitive Impairment (MCI) subjects. Quantitative structural Magnetic Resonance Imaging (MRI) acquisition methods in combination with Computer-Aided Diagnosis (CAD) are currently being used for the assessment AD. These acquisitions methods include: i) Voxel-based Morphometry (VBM), ii) volumetric measurements in specific Regions of Interest (ROIs), iii) cortical thickness measurements, iv) shape analysis and v) texture analysis. This review evaluates the aforementioned methods in the classification of cases into one of the following 3 groups: Normal Controls (NC), MCI and AD subjects. Furthermore, the performance of the methods is assessed on the prediction of conversion from MCI to AD. In parallel, it is also assessed which ROIs are preferred in both classification and prognosis through the different states of the disease. Structural changes in the early stages of the disease are more pronounced in the Medial Temporal Lobe (MTL) especially in the entorhinal cortex, whereas with disease progression both entorhinal cortex and hippocampus offer similar discriminative power. However, for the conversion from MCI subjects to AD, entorhinal cortex provides better predictive accuracies rather than other structures, such as the hippocampus

Deep learning in medical image registration: introduction and survey

Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers