Paper Abstract and Keywords |
Presentation |
2018-11-06 18:00
A Study of an Age Estimation Method From Brain MRI Images Using 3D-CNN and Its Application Masaru Ueda, Koichi Ito (Tohoku Univ.), Kai Wu (South China Univ. of Tech.), Kazunori Sato, Yasuyuki Taki (Tohoku Univ.), Hiroshi Fukuda (Tohoku Medical and Pharmaceutical Univ.), Takafumi Aoki (Tohoku Univ.) MICT2018-57 MI2018-57 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The statistical analysis of brain MR images indicates that the human brain atrophies during normal aging process. The age of subjects can be estimated from brain images by modeling morphological changes. This model can be used to identify brain disorders by evaluating the actual and estimated age. This paper proposes an age estimation method from brain MRI images using 3D Convolutional Neural Network (3D-CNN). Through a set of experiments using T1-weighted images of patients with Alzheimer's disease, the proposed method exhibits the efficient performance on age estimation performance compared with conventional methods. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
MRI / T1-weighted image / age estimation / brain aging / deep learning / CNN / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 286, MI2018-57, pp. 79-82, Nov. 2018. |
Paper # |
MI2018-57 |
Date of Issue |
2018-10-30 (MICT, MI) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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MICT2018-57 MI2018-57 |
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