Adaptive fusion of texture-based grading for Alzheimer's disease classification

Hett, Kilian; Ta, Vinh-Thong; Manjón, José V.; Coupé, Pierrick; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1016/j.compmedimag.2018.08.002

Cited by 50 publications

(27 citation statements)

References 33 publications

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“…Once the images are segmented, a patch-based grading of the entire brain is performed using the method described in [30]. Such a method was first proposed to detect hippocampus structural alterations with a new scale of analysis [15; 16].…”

Section: Patch-based Gradingmentioning

confidence: 99%

“…The most similar patches were extracted with a patch-match method [24]. We used the grading method proposed in [30], with the same parameters for the size of the patches and K xi . The effect of age has been corrected using a linear regression estimated on CN population [39].…”

Section: Details Of Implementationmentioning

confidence: 99%

“…First, we compared each element of our graph of structure grading within the hippocampal subfields (see Table 2). As previously proposed in [30], the PBG applied within the whole hippocampus is used as baseline for this experiment.…”

Section: Graph Of Hippocampal Subfieldsmentioning

confidence: 99%

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Graph of Hippocampal Subfields Grading for Alzheimer’s Disease Prediction

Hett

Manjón

et al. 2018

Machine Learning in Medical Imaging

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The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD) is clinically relevant, and may above all have a significant impact on accelerate the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to predict conversion of MCI subjects to AD accurately. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.

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Section: Patch-based Gradingmentioning

confidence: 99%

Section: Details Of Implementationmentioning

confidence: 99%

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Graph of Hippocampal Subfields Grading for Alzheimer’s Disease Prediction

Hett

Manjón

et al. 2018

Machine Learning in Medical Imaging

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“…5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 . In particular, numerous ML methods 14,15,21,28,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47 have been proposed to predict progression to AD among patients with MCI from neuroimaging data (see e.g 48,49,50 for reviews on that topic). To compare these approaches in an objective way is practically impossible, given their differences in: i) subsets of patients; ii) image preprocessing pipel...…”

Section: Introductionmentioning

confidence: 99%

Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data

Samper‐Gonzàlez

Burgos²,

Bottani

et al. 2019

Medical Imaging 2019: Image Processing

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Various machine learning methods have been proposed for predicting progression of patients with mild cognitive impairment (MCI) to Alzheimer's disease (AD) using neuroimaging data. Even though the vast majority of these works use the public dataset ADNI, reproducing their results is complicated because they often do not make available elements that are essential for reproducibility, such as selected participants and input data, image preprocessing and cross-validation procedures. Comparability is also an issue. Specially, the influence of different components like preprocessing, feature extraction or classification algorithms on the performance is difficult to evaluate. Finally, these studies rarely compare their results to models built from clinical data only, a critical aspect to demonstrate the utility of neuroimaging. In our previous work, 1, 2 we presented a framework for reproducible and objective classification experiments in AD, that included automatic conversion of ADNI database into the BIDS community standard, image preprocessing pipelines and machine learning evaluation. We applied this framework to perform unimodal classifications of T1 MRI and FDG-PET images. In the present paper, we extend this work to the combination of multimodal clinical and neuroimaging data. All experiments are based on standard approaches (namely SVM and random forests). In particular, we assess the added value of neuroimaging over using only clinical data. We first demonstrate that using only demographic and clinical data (gender, education level, MMSE, CDR sum of boxes, RAVLT, ADASCog) results in a balanced accuracy of 76% (AUC of 0.85). This performance is higher than that of standard neuroimaging-based classifiers. We then propose a simple trick to improve the performance of neuroimaging-based classifiers: training from AD patients and controls (rather than from MCI patients) improves the performance of FDG-PET classification by 5 percent points, reaching the level of the clinical classifier. Finally, combining clinical and neuroimaging data, prediction results further improved to 79% balanced accuracy and an AUC of 0.89). These prediction accuracies, obtained in a reproducible way, provide a base to develop on top of it and, to compare against, more sophisticated methods. All the code of the framework and the experiments is publicly available at https: //gitlab.icm-institute.org/aramislab/AD-ML.

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“…[11]Gabor filters textures are used to extract texture data of AD and CN images. [6] Brain topological organization captured by a novel brain model using multiplex graph network to summarize NC, AD and MCI. [12]…”

Section: Introductionmentioning

confidence: 99%

Alzheimer’s Disease Classification using Leung-Malik Filtered Bank Features and Weak Classifier

Basheera¹,

Ram²

2019

IJRTE

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We propose a frame work to classify Brain MRI images in to Alzheimer’s Disease (AD), Cognitive Normal (CN) and Mild Cognitive impairments (MCI). We use 114No’s of T2 weighted MRI Volumes. We extracted relative texture features from Leung-Malik Filter bank, k means is used to generate Bag of Dictionary (BoD) from LM Filtered images. We performed binary classification and Multi class Classification using different Classifiers, Adaboost Classifier gives better performance both in binary and multi class classifications in comparison with other classifiers. Performance of proposed system is enhanced than compared to the existing techniques. It has Sensitivity for AD-CN 89.8, AD-MCI 78.82, AD-CN-MCI 77.77, Specificity for AD-CN79.22, AD-MCI 80.00, AD-CN-MCI 58.88, and positive prediction value for AD-CN 79.48, AD-MCI 83.75, AD-CN-MCI 68.47 and Accuracy AD-CN 84.24, AD-MCI 79.33, AD-CN-MCI 72.88.

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Adaptive fusion of texture-based grading for Alzheimer's disease classification

Cited by 50 publications

References 33 publications

Graph of Hippocampal Subfields Grading for Alzheimer’s Disease Prediction

Graph of Hippocampal Subfields Grading for Alzheimer’s Disease Prediction

Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data

Alzheimer’s Disease Classification using Leung-Malik Filtered Bank Features and Weak Classifier

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