Increasing Power to Predict Mild Cognitive Impairment Conversion to Alzheimer's Disease Using Hippocampal Atrophy Rate and Statistical Shape Models

被引:0
|
作者
Leung, Kelvin K. [1 ,2 ]
Shen, Kai-Kai [3 ,4 ]
Barnes, Josephine [2 ]
Ridgway, Gerard R. [1 ,2 ]
Clarkson, Matthew J. [1 ,2 ]
Fripp, Jurgen [3 ]
Salvado, Olivier [3 ]
Meriaudeau, Fabrice [4 ]
Fox, Nick C. [2 ]
Bourgeat, Pierrick [3 ]
Ourselin, Sebastien [1 ,2 ]
机构
[1] UCL, Ctr Med Image Comp, Mortimer St, London WC1E 6BT, England
[2] UCL Inst Neurol, Dementia Res Ctr, Mortimer St, London WC1E 6BT, England
[3] CSIRO ICT, Australian Hlth Res Ctr, Floreat, Australia
[4] Univ Bourgogne, UMR, Le2i, CNRS 5158,, Dijon, France
基金
英国医学研究理事会;
关键词
VOLUME; MRI; SEGMENTATION; MORPHOMETRY; ADNI;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Identifying mild cognitive impairment (MCI) subjects who will convert to clinical Alzheimer's disease (AD) is important for therapeutic decisions, patient counselling and clinical trials. Hippocampal volume and rate of atrophy predict clinical decline at the MCI stage and progression to AD. In tins paper, we create p-maps from the differences in the shape of the hippocampus between 60 normal controls and 60 AD subjects using statistical shape models, and generate different regions of interest; (ROI) by thresholding the p-maps at different significance levels. We demonstrate increased statistical power to classify 86 MCI converters and 128 MCI stable subjects using the hippocampal atrophy rates calculated by the boundary shift integral within these ROIs.
引用
收藏
页码:125 / +
页数:3
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