使用功能数据、解剖特征和三维不变形状描述符对痴呆症进行多模态分类
MULTIMODAL CLASSIFICATION OF DEMENTIA USING FUNCTIONAL DATA, ANATOMICAL FEATURES AND 3D INVARIANT SHAPE DESCRIPTORS.
作者信息
Mikhno Arthur, Nuevo Pablo Martinez, Devanand Davangere P, Parsey Ramin V, Laine Andrew F
机构信息
Department of Biomedical Engineering, Columbia University.
Department of Electrical Engineering, Columbia University.
出版信息
Proc IEEE Int Symp Biomed Imaging. 2012:606-609. doi: 10.1109/ISBI.2012.6235621.
Multimodality classification of Alzheimer's disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), is of interest to the medical community. We improve on prior classification frameworks by incorporating multiple features from MRI and PET data obtained with multiple radioligands, fluorodeoxyglucose (FDG) and Pittsburg compound B (PIB). We also introduce a new MRI feature, invariant shape descriptors based on 3D Zernike moments applied to the hippocampus region. Classification performance is evaluated on data from 17 healthy controls (CTR), 22 MCI, and 17 AD subjects. Zernike significantly outperforms volume, accuracy (Zernike to volume): CTR/AD (90.7% to 71.6%), CTR/MCI (76.2% to 60.0%), MCI/AD (84.3% to 65.5%). Zernike also provides comparable and complementary performance to PET. Optimal accuracy is achieved when Zernike and PET features are combined (accuracy, specificity, sensitivity), CTR/AD (98.8%, 99.5%, 98.1%), CTR/MCI (84.3%, 82.9%, 85.9%) and MCI/AD (93.3%, 93.6%, 93.3%).
阿尔茨海默病（AD）及其前驱阶段轻度认知障碍（MCI）的多模态分类受到医学界的关注。我们通过整合来自MRI和PET数据的多个特征来改进先前的分类框架，这些数据是使用多种放射性配体、氟脱氧葡萄糖（FDG）和匹兹堡化合物B（PIB）获得的。我们还引入了一种新的MRI特征，即基于应用于海马体区域的3D泽尼克矩的不变形状描述符。在来自17名健康对照（CTR）、22名MCI患者和17名AD患者的数据上评估分类性能。泽尼克描述符的表现显著优于体积指标，准确率（泽尼克描述符相对于体积指标）：CTR/AD（90.7%对71.6%），CTR/MCI（76.2%对60.0%），MCI/AD（84.3%对65.5%）。泽尼克描述符还提供了与PET相当且互补的性能。当泽尼克描述符和PET特征相结合时可实现最佳准确率（准确率、特异性、敏感性），CTR/AD（98.8%、99.5%、98.1%），CTR/MCI（84.3%、82.9%、85.9%）以及MCI/AD（93.3%、93.6%、93.3%）。
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