@inproceedings{974edc9c1d634e24a6613c7f63d6b398,
title = "An age estimation method using 3d-cnn from brain MRI images",
abstract = "A specific pattern of morphological changes in the human brain is observed during the process of brain development and healthy aging. The age of subjects can be estimated from brain images by evaluating such patterns. This paper proposes an age estimation method using 3-Dimensional Convolutional Neural Network (3D-CNN) from brain T1-weighted images so as to fully utilize the potential of volume data. Through a set of experiments using over 1,000 T1-weighted images of healthy Japanese, we demonstrate that the proposed method exhibits better performance on age estimation than the conventional methods using handcrafted local features and 2D-CNN.",
keywords = "Age estimation, Brain aging, CNN, Deep learning, MRI, T1-weighted image",
author = "Masaru Ueda and Koichi Ito and Kai Wu and Kazunori Sato and Yasuyuki Taki and Hiroshi Fukuda and Takafumi Aoki",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",
year = "2019",
month = apr,
doi = "10.1109/ISBI.2019.8759392",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "380--383",
booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",
address = "United States",
}
