Recognition of Alzheimer's disease and Mild Cognitive Impairment with multimodal image-derived biomarkers and Multiple Kernel Learning
Recognition of Alzheimer's disease and Mild Cognitive Impairment with multimodal image-derived biomarkers and Multiple Kernel Learning
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DOI：
10.1016/j.neucom.2016.08.041
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发表时间：
2017-01-12
期刊：
影响因子：
6
通讯作者：
Ben Amar, Chokri
中科院分区：
文献类型：
--
作者：
Ben Ahmed, Olfa;Benois-Pineau, Jenny;Ben Amar, Chokri
阿尔茨海默病（AD）的计算机辅助诊断（CAD）在过去几年中引起了计算机视觉研究界的关注。已经进行了几次尝试，以适应特定的神经成像数据，如结构MRI（sMRI）的早期AD诊断模式识别方法。一种策略是通过在一个单一的学习框架中整合互补的成像方式来提高这种方法的识别能力。扩散张量成像（DTI）是一种新的和有前途的方式提供补充信息的解剖MRI。然而，包括相关的DTI信息，从这样的方式是一个具有挑战性的问题。在本文中，我们建议从DTI和sMRI中提取局部图像衍生的生物标志物来构建多模态AD签名。为了评估这些模式的相关性以及优化分类器，我们使用多核学习（MKL）框架进行AD主题识别来整合互补信息。为了评估我们的方法，我们对阿尔茨海默病神经成像倡议（ADNI）数据集的一个子集进行实验。使用了45名AD患者、52名正常对照（NC）和58名轻度认知障碍（MCI）受试者的DTI模式的T1加权MRI和平均扩散率（MD）图。所获得的结果表明，我们的多模态方法产生了显着的改善，在使用每个单一的方式独立的准确性。对AD vs. NC、MCI vs. NC和AD vs. MCI二元分类问题，该方法的分类正确率分别为90.2%、79.42%和76.63%。对于MCI分类问题，所提出的融合框架导致准确性平均增加至少9%，特异性平均增加5%，灵敏度平均增加15%。(C)2016爱思唯尔B.V.保留所有权利。
Computer-Aided Diagnosis (CAD) of Alzheimer's disease (AD) has drawn the attention of computer vision research community over the last few years. Several attempts have been made to adapt pattern recognition approaches to specific neuroimaging data such as Structural MRI (sMRI) for early AD diagnosis. One strategy is to boost the discrimination power of such approaches by integrating complementary imaging modalities in a single learning framework. Diffusion Tensor Imaging (DTI) is a new and promising modality giving complementary information to the anatomical MRI. However, including relevant DTI information from such modality is a challenging problem. In this paper, we propose to extract local image-derived biomarkers from DTI and sMRI to construct multimodal AD signatures. To assess the relevance of such modalities as well as to optimize the classifier, we integrate complementary information using a Multiple Kernel Learning (MKL) framework for AD subjects recognition. To evaluate our method, we perform experiments on a subset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Both T1-weighted MRI and Mean Diffusivity (MD) maps from the DTI modality of 45 AD patients, 52 Normal Control (NC) and 58 Mild Cognitive Impairment (MCI) subjects have been used. The obtained results indicate that our multimodal approach yields significant improvement in accuracy over using each single modality independently. The classification accuracies obtained by the proposed method are 90.2%, 79.42% and 76.63% for respectively AD vs. NC, MCI vs. NC and AD vs. MCI binary classification problems. For the MCI classification problem, the proposed fusion framework leads to an average increase about at least 9% for the accuracy, 5% for the specificity and 15% for the sensitivity. (C) 2016 Elsevier B.V. All rights reserved.