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Transfer learning on T1-weighted images for brain age estimation

1 University of Science and Technology of China, Hefei, Anhui 230026, China
2 School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009, China

Special Issues: Advanced Computer Methods and Programs in Biomedicine

Due to both the hidden nature and the irreversibility of Alzheimers disease (AD), it has become the killer of the elderly and is thus the focus of much attention in the medical field. Radiologists compare the predicted brain age with the ground truth in order to provide a preliminary analysis of AD, which helps doctors diagnose AD as early in its development as possible. In this paper, a transfer learning-based method of predicting brain age using MR images and dataset of a public brain was proposed. In order to get the best transfer results, we froze different layers and only fine-tuned the remaining layers. We used three planes of brain MR images together to predict age for the first time and experiment results showed that the proposed method performs better than the state-of-the-art method under mean absolute error metric by 0.6 years. In addition, to explore the relationship between brain MR images of different planes and predicted age accuracy, we used three different planes of brain MR images to predict age respectively for the first time and found that sagittal plane MR images outperformed two other planes in age estimation. Finally, our research identified, the effective regions that contribute to brain age estimation for cognitively normal individuals and for AD patients with deep learning. For AD patients, the effective region is mainly concentrated in the frontal lobe of the brain, verifying the relevant medical conclusions about AD.
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Keywords Alzheimer’s disease; brain age; MR images; transfer learning; frontal lobe

Citation: Haitao Jiang, Jiajia Guo, Hongwei Du, Jinzhang Xu, Bensheng Qiu. Transfer learning on T1-weighted images for brain age estimation. Mathematical Biosciences and Engineering, 2019, 16(5): 4382-4398. doi: 10.3934/mbe.2019218


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