Survey on Early Detection of Alzhiemer’s Disease Using Capsule Neural Network

Alzheimer's disease (AD) is an disorder which is irreversible of the brain related to memory loss, mostly found in the old and aged population. Alzheimer's dementia results from the degeneration or loss of brain cells. The brain-imaging technologies most often used to diagnose AD is Magnetic resonance imaging (MRI). MRI or structural magnetic resonance is a very popular and actual technique used to diagnose AD. An MRI uses magnets and powerful radio waves to create a complete view of your brain. To actually detect the presence of Alzheimer’s, the MRI should me studied carefullyImplementation of CBIR Content Based Image Retrival which is a revolutionary computer aided diagnosis technique will create new abilities in MRI Magnetic resonance imaging in related image retrieval and training for recognition of development of AD in early stage

Surface fluid registration of conformal representation: Application to detect disease burden and genetic influence on hippocampus

abstract: In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometty (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E(is an element of)4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.NOTICE: this is the author’s version of a work that was accepted for publication in NEUROIMAGE. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neuroimage, 78, 111-134 [2013] http://dx.doi.org/10.1016/j.neuroimage.2013.04.01

Whole-brain patterns of 1H-magnetic resonance spectroscopy imaging in Alzheimer's disease and dementia with Lewy bodies

Acknowledgements We thank Craig Lambert for his help in processing the MRS data. The study was funded by the Sir Jules Thorn Charitable Trust (grant ref: 05/JTA) and was supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre and the Biomedical Research Unit in Lewy Body Dementia based at Newcastle upon Tyne Hospitals National Health Service (NHS) Foundation Trust and Newcastle University and the NIHR Biomedical Research Centre and Biomedical Research Unit in Dementia based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge.Peer reviewedPublisher PD

Towards Practical Application of Deep Learning in Diagnosis of Alzheimer's Disease

Accurate diagnosis of Alzheimer's disease (AD) is both challenging and time consuming. With a systematic approach for early detection and diagnosis of AD, steps can be taken towards the treatment and prevention of the disease. This study explores the practical application of deep learning models for diagnosis of AD. Due to computational complexity, large training times and limited availability of labelled dataset, a 3D full brain CNN (convolutional neural network) is not commonly used, and researchers often prefer 2D CNN variants. In this study, full brain 3D version of well-known 2D CNNs were designed, trained and tested for diagnosis of various stages of AD. Deep learning approach shows good performance in differentiating various stages of AD for more than 1500 full brain volumes. Along with classification, the deep learning model is capable of extracting features which are key in differentiating the various categories. The extracted features align with meaningful anatomical landmarks, that are currently considered important in identification of AD by experts. An ensemble of all the algorithm was also tested and the performance of the ensemble algorithm was superior to any individual algorithm, further improving diagnosis ability. The 3D versions of the trained CNNs and their ensemble have the potential to be incorporated in software packages that can be used by physicians/radiologists to assist them in better diagnosis of AD.Comment: 18 pages, 8 figure

Whole brain white matter histogram analysis of diffusion tensor imaging data detects microstructural damage in mild cognitive impairment and Alzheimer’s disease patients

ABSTRACT Background: Amnestic mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer’s disease (AD). However, the clinical conversion from MCI to AD is unpredictable. Hence, identification of non-invasive biomarkers able to detect early changes induced by dementia is a pressing need. Purpose: To explore the added value of histogram analysis applied to measures derived from diffusion tensor imaging (DTI) for detecting brain tissue differences between AD, MCI and healthy subjects (HS). Study type: Retrospective. Population/subjects: Local cohort (57 AD, 28 MCI, 23 HS), Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort (41 AD, 58 MCI, 41 HS). Field Strength: 3T. Dual echo TSE; FLAIR; MDEFT; IR-SPGR; DTI. Assessment: Normal appearing white matter (NAWM) masks were obtained using the T1-weighted volumes for tissue segmentation and T2-weighted images for removal of hyperintensities/lesions. From DTI images, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AXD) and radial diffusivity (RD) were obtained. NAWM histograms of FA, MD, AXD and RD were derived and characterized estimating: peak height, peak location, mean value (MV), and quartiles (C25, C50, C75), which were compared between groups. Receiver operating characteristic (ROC) and area under ROC curves (AUC) were calculated. To confirm our results, the same analysis was repeated on ADNI dataset. Statistical tests: One-way ANOVA, post-hoc Student’s t-test, multi-class ROC analysis. Results: For the local cohort, C25 of AXD had the maximum capability of group discrimination with AUC of 0.80 for “HS vs patients” comparison and 0.74 for “AD vs others” comparison. For ADNI cohort, MV of AXD revealed the maximum group discrimination capability with AUC of 0.75 for “HS vs patients” comparison and 0.75 for “AD vs others” comparison. Data conclusion: AXD of NAWM might be an early marker of microstructural brain tissue changes occurring during AD course and might be useful for assessing disease progression

Hierarchical Feature Extraction for Early Alzheimer’s Disease Diagnosis

Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). In this article, we propose a novel voxel-based hierarchical feature extraction (VHFE) method for the early AD diagnosis. First, we parcellate the whole brain into 90 regions of interests (ROIs) based on an Automated Anatomical Labeling (AAL) template. To split the uninformative data, we select the informative voxels in each ROI with a baseline of their values and arrange them into a vector. Then, the first stage features are selected based on the correlation of the voxels between different groups. Next, the brain feature maps of each subjects made up of the fetched voxels is fed into a convolutional neural network (CNN) to learn the deep hidden features. Finally, to validate the effectiveness of the proposed method, we test it with the subset of the Alzheimer’s Disease Neuroimaging (ADNI) database. The testing results demonstrate that the proposed method is robust with promising performance in comparison with the state-of-the-art methods.Science and Technology Commission of Shanghai Municipality under Grant 16JC1401300, Grant 7ZR1431600, and Grant 18ZR1442700; Shanghai Sailing Program under Grant 16YF1415300; Special Fund for Basic Scientific Research Business Expenses of Central Colleges and Universities under Grant 22120180542; Fundamental Research Funds for the Central Universities

Automatic detection of disorientation among people with dementia

Ageing is characterized by decline in cognition including visuospatial function, necessary for independently executing instrumental activities of daily living. The onset of Alzheimer’s disease dementia exacerbates this decline, leading to major challenges for patients and increased burden for caregivers. An important function affected by this decline is spatial orientation. This work provides insight into substrates of real-world wayfinding challenges among older adults, with emphasis on viable features aiding the detection of spatial disorientation and design of possible interventions

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Diffuse white matter loss in a transgenic rat model of cerebral amyloid angiopathy

Diffuse white matter (WM) disease is highly prevalent in elderly with cerebral small vessel disease (cSVD). In humans, cSVD such as cerebral amyloid angiopathy (CAA) often coexists with Alzheimer’s disease imposing a significant impediment for characterizing their distinct effects on WM. Here we studied the burden of age-related CAA pathology on WM disease in a novel transgenic rat model of CAA type 1 (rTg-DI). A cohort of rTg-DI and wild-type rats was scanned longitudinally using MRI for characterization of morphometry, cerebral microbleeds (CMB) and WM integrity. In rTg-DI rats, a distinct pattern of WM loss was observed at 9 M and 11 M. MRI also revealed manifestation of small CMB in thalamus at 6 M, which preceded WM loss and progressively enlarged until the moribund disease stage. Histology revealed myelin loss in the corpus callosum and thalamic CMB in all rTg-DI rats, the latter of which manifested in close proximity to occluded and calcified microvessels. The quantitation of CAA load in rTg-DI rats revealed that the most extensive microvascular Aβ deposition occurred in the thalamus. For the first time using in vivo MRI, we show that CAA type 1 pathology alone is associated with a distinct pattern of WM loss