Article

Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI

Université Pierre et Marie Curie-Paris6, CNRS, UMR-S7225, Paris, France.
Hippocampus (Impact Factor: 4.3). 06/2009; 19(6):579-87. DOI: 10.1002/hipo.20626
Source: PubMed

ABSTRACT The hippocampus is among the first structures affected in Alzheimer's disease (AD). Hippocampal magnetic resonance imaging volumetry is a potential biomarker for AD but is hindered by the limitations of manual segmentation. We proposed a fully automatic method using probabilistic and anatomical priors for hippocampus segmentation. Probabilistic information is derived from 16 young controls and anatomical knowledge is modeled with automatically detected landmarks. The results were previously evaluated by comparison with manual segmentation on data from the 16 young healthy controls, with a leave-one-out strategy, and eight patients with AD. High accuracy was found for both groups (volume error 6 and 7%, overlap 87 and 86%, respectively). In this article, the method was used to segment 145 patients with AD, 294 patients with mild cognitive impairment (MCI), and 166 elderly normal subjects from the Alzheimer's Disease Neuroimaging Initiative database. On the basis of a qualitative rating protocol, the segmentation proved acceptable in 94% of the cases. We used the obtained hippocampal volumes to automatically discriminate between AD patients, MCI patients, and elderly controls. The classification proved accurate: 76% of the patients with AD and 71% of the MCI converting to AD before 18 months were correctly classified with respect to the elderly controls, using only hippocampal volume.

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Available from: Louis Lemieux, Jan 15, 2014
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    • "The hippocampal formation consists of a number of distinct, interacting subregions , which comprise a complex, heterogeneous structure. Despite its internal complexity, limits in MRI resolution have traditionally forced researchers to model the hippocampus as a single, homogeneous structure in neuroimaging studies of aging and AD (Boccardi et al., 2011; Chupin et al., 2009). Even though these studies have shown that whole hippocampal volumes derived from automatically or manually segmented MRI scans are powerful biomarkers for AD (Convit et al., 1997; Jack et al., 1999; Frisoni et al., 1999; De Toleto-Morrell et al., 2000; den Heijer et al., 2006; Wang et al., 2003; Fischl et al., 2002), treating the hippocampus as a single entity disregards potentially useful information about its subregions. "
    NeuroImage 07/2015; 115(July, 15):117-137. · 6.36 Impact Factor
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    • "Most of the earlier ROI based methods are based on manual segmentation of the region of interest. The features extracted in these techniques are usually tissue densities [18], cortical thickness [19] [20], and volume and shape of hippocampus [21] [22]. The limitation of such technique is that they do not show high sensitivity and specificity in diagnosis of individuals because of the complex pathology of AD. "
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    ABSTRACT: 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.
    Computational and Mathematical Methods in Medicine 09/2014; 2014:862307. DOI:10.1155/2014/862307 · 1.02 Impact Factor
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    • "67 [42–85] 78 [60–92] ++++ 79 [64–91] ++++ MMSE: mean (range) 27 [6–30] 25 [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] 21 [13–29] Clock drawing test mean (range) 6 [0] [1] [2] [3] [4] [5] [6] [7] 5 [2] [3] [4] [5] [6] [7] 4 [0] [1] [2] [3] [4] [5] [6] [7] GMV [ml] "
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