Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging

By Emilie Gerardin, Gaël Chételat, Marie Chupin, Rémi Cuingnet, B. Desgranges, Hosung Kim, Marc Niethammer, Bruno Dubois, Stéphane Lehéricy, Line Garnero and 2 more. NeuroImage, 2009

428 citations recorded by the source corpus.

We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). The most relevant features for classification are selected using a bagging strategy. We evaluate the accuracy of our method in a group of 23 patients with AD (10 males, 13 females, age±standard-deviation (SD)=73±6 years, mini-mental score (MMS)=24.4±2.8), 23 patients with amnestic MCI (10 males, 13 females, age±SD=74±8 years, MMS=27.3±1.4) and 25 elderly healthy controls (13 males,12 females, age±SD=64±8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease.

Subfields: Neurology, Physiology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/9687bd297bee9899b6ee283f558d3ad89953d1e7

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Citations in the corpus

Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment (2011, methodology): “…1) voxel-wise tissue probability (Fan et al., 2007; Kloppel et al., 2008; Lao et al., 2004; Magnin et al., 2009), 2) cortical thickness (Desikan et al., 2009; Lerch et al., 2008; Oliveira et al., 2010; Querbes et al., 2009), and 3) hippocampal volumes (Gerardin et al., 2009; West et al., 2004).”
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database (2011, methodology): “In this paper, we compared different methods for the classification of patients with AD and MCI based on anatomical T1-weighted MRI.”
A Bayesian model of shape and appearance for subcortical brain segmentation (2011, positive): “In addition, the changes in the tail of the hippocampus are consistent with previous studies showing regional atrophy in CA1 that corresponds with the medial aspect of the head and lateral aspect of the body extending to the tail (Gerardin et al., 2009).”
A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease (2019, neutral): “Since the MRI datasets used for AD diagnosis are typically very small compared with the datasets used in computer vision, it remains a major challenge to train a deeper CNN model with a large number of parameters to be learned (Gray et al., 2011).”
Prediction of MCI to AD conversion, via MRI, CSF biomarkers, pattern classification (2010, methodology): “…found in recent years to be promising ools for capturing such complex spatial patterns of brain tructure (Davatzikos et al., 2009; Duchesne et al., 2008; an et al., 2008b; Gerardin et al., 2009; Hinrichs et al., 009; Kloppel et al., 2008; Lao et al., 2004; McEvoy et al., 009; Vemuri et al., 2009).”
A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, positive): “(Gerardin et al., 2009) 23 23 – – 25 Hippocampus SVM HCUDC 94.”
Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease (2011, methodology): “Gerardin et al. [23] acquired a high SEN/SPE of 96/92 by using hippocampal shape analysis, but the number of subjects (25 HC, 23 AD) was quite low in order to produce results with good generalizability.”
Alzheimer's disease diagnostics by adaptation of 3D convolutional network (2016, background): “[18] segmented and spatially aligned the hippocampus regions for various subjects and modeled their shape with a series of spherical harmonics.”
Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images (2012, methodology): “The imaging data came from the Alzheimer's disease Neuroimaging Initiative (ADNI), which have been used by many studies of AD classification (Filipovych and Davatzikos, 2011; Gerardin et al., 2009; PelaezCoca et al., 2011; Stonnington et al., 2010; Yang et al., 2011; Zhang et al., 2011).”