Abstract #3763

Deep Learning-based MRI Image Analysis for the Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease

Weiming Lin^1,2, Min Du¹, Di Guo³, Xiaofeng Du³, Yonggui Yang⁴, Gang Guo⁴, and Xiaobo Qu⁵

¹College of Physics and Information Engineering, Fuzhou University, Fuzhou, China, ²School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China, ³School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, ⁴Department of Radiology, Xiamen 2nd Hospital, Xiamen, Xiamen, China, ⁵Department of Electronic Science, Xiamen University, Xiamen, China

Accurate prediction of the conversion from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is critically important to slow down the progression to AD with early clinical trials. In this work, this prediction for 3 years is conducted on MRI images shared in Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Two powerful image analysis tools, including convolutional neural networks in deep learning and FreeSurfer in brain MRI analysis, are introduced to learn image features which are used for further classification. Cross validation results demonstrate that the proposed approach achieves more accurate and robust prediction comparing with the state-of-the-art grading biomarker method.

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