A novel Convolutional Neural Network Model Based on Voxel-based Morphometry of Imaging Data in Predicting the Prognosis of Patients with Mild Cognitive Impairment
Journal of Neurological Sciences-Turkish, 2017, 34(1): 52-69.
Objective: Nowadays, it is of great interest to identify neuroimaging biomarkers for the early detection of Alzheimer's disease (AD). It is considered that approximately half of patients with a diagnosis of mild cognitive impairment (MCI) eventually develop Alzheimer's disease, and the other half remain stable. In this context, a novel convolutional neural network (CNN) based on voxel-based morphometric analysis is proposed to predict the prognosis of patients with MCI using their baseline structural magnetic resonance (MR) images.
Methods: Two groups of patients were identified among 305 patients with a diagnosis of MCI, those who developed Alzheimer's disease during their follow-up (n=140), and those who remained stable in the MCI state (n=165). The baseline structural MR images of the patients were used for training and evaluating the proposed prediction model. Voxel-based morphometry generated from the baseline structural MR images was used to obtain significant volume of interests (VOIs) related with gray matter damage. Then, a convolutional neural network was trained to extract prognostic features from MR images using a set of convolutional feature detectors acquired by the training of a patch-based autoencoder.
Results: This work achieved an accuracy of 78.7%, slightly superior (more than 4%) to a reference study, for predicting the risk of developing Alzheimer's disease for patients with MCI.
Conclusion: The results of this study show that the use of a convolutional neural network using significant topographic regions of the brain is successful in predicting the risk of developing Alzheimer's disease for patients with MCI.
Convolutional Neural Network; Alzheimer's disease; Voxel-based Morphometry; Pooling