Automated Method for Identification of Patients With Alzheimer’s Disease Based on Three-dimensional MR Images

2008 ◽  
Vol 15 (3) ◽  
pp. 274-284 ◽  
Author(s):  
Hidetaka Arimura ◽  
Takashi Yoshiura ◽  
Seiji Kumazawa ◽  
Kazuhiro Tanaka ◽  
Hiroshi Koga ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Mr Images ◽  
Automated Method

3D MR Models and Volumetric Measurements of the Brain in Patients with Alzheimer's Disease

2008 ◽  
Vol 21 (5) ◽  
pp. 611-617
Author(s):  
P. Papapostolou ◽  
M. Arvaniti ◽  
F. Goutsaridou ◽  
M. Emmanouilidou ◽  
G. Tezapsidis ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Brain Structures ◽  
3D Models ◽  
Brain Parenchyma ◽  
Mr Images ◽  
Whole Brain ◽  
The Brain ◽  
Volumetric Measurements

This study aimed to describe the procedure we use to create 3D models of the brain parenchyma from MRI images and calculate the volume of the whole brain and different compartments of the brain in patients with Alzheimer's disease. The utility of the 3D models and volumetric measurements of the whole brain parenchyma and different brain structures is discussed. Thirty-six patients with Alzheimer's disease were examined during the last six months with MRI. Fourteen of them were men and 22 were women. The patients were between 53 and 67 years old. MR images were studied using an automatic algorithm. The images from MRI were segmented and then three-dimensional models of brain were produced to calculate the brain volume and the volume of the white matter, gray matter and CSF separately. The whole procedure was completed successfully in 34 patients. The procedure was unsuccessful in two patients due to movement artifacts in MR images. It is relatively easy to create 3D models of MR images and to obtain volumetric studies. If this procedure is adjusted in patients with Alzheimer's disease, we can provide information more clearly and accurately than single images alone. The information obtained can be used in daily clinical practice such as pharmaceutical treatment planning and results or in basic clinical research.

Computer-aided methods for 3-D visualization of serial sections and thick biological specimens

1992 ◽  
Vol 50 (2) ◽  
pp. 1060-1061
Author(s):  
Mark Ellisman ◽  
Maryann Martone ◽  
Gabriel Soto ◽  
Eleizer Masliah ◽  
David Hessler ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Neuritic Plaque ◽  
Dimensional Structure ◽  
Data Sets ◽  
Molecular Physiology ◽  
Research Activities ◽  
Computer Aided ◽  
Dimensional Reconstruction

Structurally-oriented biologists examine cells, tissues, organelles and macromolecules in order to gain insight into cellular and molecular physiology by relating structure to function. The understanding of these structures can be greatly enhanced by the use of techniques for the visualization and quantitative analysis of three-dimensional structure. Three projects from current research activities will be presented in order to illustrate both the present capabilities of computer aided techniques as well as their limitations and future possibilities.The first project concerns the three-dimensional reconstruction of the neuritic plaques found in the brains of patients with Alzheimer's disease. We have developed a software package “Synu” for investigation of 3D data sets which has been used in conjunction with laser confocal light microscopy to study the structure of the neuritic plaque. Tissue sections of autopsy samples from patients with Alzheimer's disease were double-labeled for tau, a cytoskeletal marker for abnormal neurites, and synaptophysin, a marker of presynaptic terminals.

Three-Dimensional Chromatin Architecture Landscape of Aging and Alzheimer's Disease

2020 ◽  
Author(s):  
Guofeng Meng ◽  
Jialan Huang ◽  
Dong Lu ◽  
Zhenzhen Zhao ◽  
Feng Yu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Chromatin Architecture

scholarly journals A Super-Resolved View of the Alzheimer’s Disease-Related Amyloidogenic Pathway in Hippocampal Neurons

2021 ◽  
pp. 1-20
Author(s):  
Yang Yu ◽  
Yang Gao ◽  
Bengt Winblad ◽  
Lars Tjernberg ◽  
Sophia Schedin Weiss
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hippocampal Neurons ◽  
Three Dimensional ◽  
Amyloid Β ◽  
Stimulated Emission ◽  
Amyloid Β Peptide ◽  
Primary Neurons ◽  
Amyloid Β Protein ◽  
Early Endosomes

Background: Processing of the amyloid-β protein precursor (AβPP) is neurophysiologically important due to the resulting fragments that regulate synapse biology, as well as potentially harmful due to generation of the 42 amino acid long amyloid β-peptide (Aβ 42), which is a key player in Alzheimer’s disease. Objective: Our aim was to clarify the subcellular locations of the amyloidogenic AβPP processing in primary neurons, including the intracellular pools of the immediate substrate, AβPP C-terminal fragment (APP-CTF) and the product (Aβ 42). To overcome the difficulties of resolving these compartments due to their small size, we used super-resolution microscopy. Methods: Mouse primary hippocampal neurons were immunolabelled and imaged by stimulated emission depletion (STED) microscopy, including three-dimensional, three-channel imaging and image analyses. Results: The first (β-secretase) and second (γ-secretase) cleavages of AβPP were localized to functionally and distally distinct compartments. The β-secretase cleavage was observed in early endosomes, where we were able to show that the liberated N- and C-terminal fragments were sorted into distinct vesicles budding from the early endosomes in soma. Lack of colocalization of Aβ 42 and APP-CTF in soma suggested that γ-secretase cleavage occurs in neurites. Indeed, APP-CTF was, in line with Aβ 42 in our previous study, enriched in the presynapse but absent from the postsynapse. In contrast, full-length AβPP was not detected in either the pre- or the postsynaptic side of the synapse. Furthermore, we observed that endogenously produced and endocytosed Aβ 42 were localized in different compartments. Conclusion: These findings provide critical super-resolved insight into amyloidogenic AβPP processing in primary neurons.

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

Automatic Diagnosis of Alzheimer’s Disease Using Deep Learning Techniques

2021 ◽  
Vol 19 (11) ◽  
pp. 126-140
Author(s):  
Zahraa S. Aaraji ◽  
Hawraa H. Abbas
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Data Analysis ◽  
Binary Classification ◽  
Brain Mri ◽  
Three Dimensional ◽  
Structural Variations ◽  
Automatic Feature Extraction ◽  
Multi Class Classification

Neuroimaging data analysis has attracted a great deal of attention with respect to the accurate diagnosis of Alzheimer’s disease (AD). Magnetic Resonance Imaging (MRI) scanners have thus been commonly used to study AD-related brain structural variations, providing images that demonstrate both morphometric and anatomical changes in the human brain. Deep learning algorithms have already been effectively exploited in other medical image processing applications to identify features and recognise patterns for many diseases that affect the brain and other organs; this paper extends on this to describe a novel computer aided software pipeline for the classification and early diagnosis of AD. The proposed method uses two types of three-dimensional Convolutional Neural Networks (3D CNN) to facilitate brain MRI data analysis and automatic feature extraction and classification, so that pre-processing and post-processing are utilised to normalise the MRI data and facilitate pattern recognition. The experimental results show that the proposed approach achieves 97.5%, 82.5%, and 83.75% accuracy in terms of binary classification AD vs. cognitively normal (CN), CN vs. mild cognitive impairment (MCI) and MCI vs. AD, respectively, as well as 85% accuracy for multi class-classification, based on publicly available data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI).

Fasu-Net: Fast Alzheimer’s Disease Screening with Undersampled MRI Using Convolutional Neural Networks

2021 ◽  
Vol 11 (8) ◽  
pp. 2211-2221
Author(s):  
Yuanbo Xie ◽  
Haitao Jiang ◽  
Hongwei Du ◽  
Jinzhang Xu ◽  
Bensheng Qiu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Mri ◽  
Potential Method ◽  
Disease Screening ◽  
Acquisition Time ◽  
Mr Images ◽  
Brain Mr Images ◽  
Magnetic Resonance Imaging Mri ◽  
Questionnaire Method

Alzheimer’s Disease (AD) is a progressive and irreversible neurodegenerative condition, which results in dementia. Mild Cognitive Impairment (MCI) is an intermediate state between normal aging and AD. Instead of traditional questionnaire method, magnetic resonance imaging (MRI) can be used by radiologists to diagnose and screening AD recently, but long acquisition time is not conducive to screening AD and MCI. To solve this problem, we develop a Fasu-Net (Fast Alzheimer’s disease Screening neural network with Undersampled MRI) for AD and MCI clinical classification. The network uses undersampled structural MRI with a shorter acquisition time to improve the screening and diagnosis efficiency of AD. For achieving the best classification result, three axial planes of brain MR images were feed into the Fasu-Net with transfer learning method. The experiment results on undersampled 3D T1-weighted images database (ADNI) show that in the AD versus MCI versus HC (Healthy Controls) classification, the Fasu-Net achieved the accuracy of 91.41%, thus can be a potential method for fast clinical screening of AD.

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