TY - GEN
T1 - Performance Evaluation of Age Estimation from T1-Weighted Images Using Brain Local Features and CNN
AU - Ito, Koichi
AU - Fujimoto, Ryuichi
AU - Huang, Tzu Wei
AU - Chen, Hwann Tzong
AU - Wu, Kai
AU - Sato, Kazunori
AU - Taki, Yasuyuki
AU - Fukuda, Hiroshi
AU - Aoki, Takafumi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - The age of a subject can be estimated from the brain MR image by evaluating morphological changes in healthy aging. We consider using two-types of local features to estimate the age from T1-weighted images: handcrafted and automatically extracted features in this paper. The handcrafted brain local features are defined by volumes of brain tissues parcellated into 90 or 1,024 local regions defined by the automated anatomical labeling atlas. The automatically extracted features are obtained by using the convolutional neural network (CNN). This paper explores the difference between the handcrafted features and the automatically extracted features. Through a set of experiments using 1,099 T1-weighted images from a Japanese MR image database, we demonstrate the effectiveness of the proposed methods, analyze the effectiveness of each local region for age estimation and discuss its medical implication.
AB - The age of a subject can be estimated from the brain MR image by evaluating morphological changes in healthy aging. We consider using two-types of local features to estimate the age from T1-weighted images: handcrafted and automatically extracted features in this paper. The handcrafted brain local features are defined by volumes of brain tissues parcellated into 90 or 1,024 local regions defined by the automated anatomical labeling atlas. The automatically extracted features are obtained by using the convolutional neural network (CNN). This paper explores the difference between the handcrafted features and the automatically extracted features. Through a set of experiments using 1,099 T1-weighted images from a Japanese MR image database, we demonstrate the effectiveness of the proposed methods, analyze the effectiveness of each local region for age estimation and discuss its medical implication.
UR - http://www.scopus.com/inward/record.url?scp=85056649218&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056649218&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8512443
DO - 10.1109/EMBC.2018.8512443
M3 - Conference contribution
C2 - 30440491
AN - SCOPUS:85056649218
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 694
EP - 697
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -