Article

Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease

Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
PLoS ONE (Impact Factor: 3.23). 10/2011; 6(10):e25446. DOI: 10.1371/journal.pone.0025446
Source: PubMed

ABSTRACT

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.

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Available from: Juha Koikkalainen
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    • "Several recent studies have demonstrated that multi-template based features can contain complementary information for boosting performance of AD/MCI classification[14,15,17,18,29,30]. However, the main disadvantage of these existing methods is that the structural information in multi-template data is seldom considered, which may lead to sub-optimal learning performance. "
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    ABSTRACT: As shown in the literature, methods based on multiple templates usually achieve better performance, compared to those using only a single template for processing medical images. However, most existing multi-template based methods simply average or concatenate multiple sets of features extracted from different templates, which potentially ignoring important structural information contained in the multi-template data. Accordingly, in this paper, we propose a novel relationship induced multi-template learning method for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI), by explicitly modeling structural information in the multi-template data. Specifically, we first nonlinearly register each brain's magnetic resonance (MR) image separately onto multiple pre-selected templates, and then extract multiple sets of features for this MR image. Next, we develop a novel feature selection algorithm by introducing two regularization terms to model the relationships among templates and among individual subjects. Using these selected features corresponding to multiple templates, we then construct multiple support vector machine (SVM) classifiers. Finally, an ensemble classification is used to combine outputs of all SVM classifiers, for achieving the final result. We evaluate our proposed method with 459 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 97 AD patients, 128 normal controls (NC), 117 progressive MCI (pMCI) patients, and 117 stable MCI (sMCI) patients. The experimental results demonstrate promising classification performance, compared with several state-of-the-art methods for multi-template based AD/MCI classification.
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    • "In the literature, many brain morphometric pattern analysis methods have been developed for computer-aided AD/MCI diagnosis, by identifying differences in shape and neuroanatomical configuration of different brains provided by magnetic resonance imaging (MRI) [2] [3] [4] [5] [6] [7] [8] [9] [10]. Most of early works use regional measurement of anatomical volumes in pre-defined regions of interest (ROIs) (e.g., hippocampus, entorhinal cortex, or neocortex) to investigate abnormal tissue structure caused by AD or MCI. "
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    • ") were used , which is a common practice in studies of computer - aided diagnosis methods ( Cuingnet et al . , 2011 ; Klöppel et al . , 2008 ; Falahati et al . , 2014 ; Davatzikos et al . , 2008a ; Duchesne et al . , 2008 ; Fan et al . , 2008a , b ; Gray et al . , 2013 ; Koikkalainen et al . , 2012 ; Magnin et al . , 2009 ; Vemuri et al . , 2008 ; Wolz et al . , 2011 ) . Ground truth diagnosis of dementia can only be assessed using autopsy and is therefore only rarely available . Of the previously mentioned pa - pers , only one paper included one group of 20 AD patients with an au - topsy confirmed diagnosis ( Klöppel et al . , 2008 ) . Amyloid imaging ( Klunk et al . , 2004 ) has also proven to be "
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    ABSTRACT: Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi- center data set. Using clinical practice as starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer’s disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with in total 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer’s Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.
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