Age estimation from brain MRI images using deep learning

Tzu Wei Huang, Hwann Tzong Chen, Ryuichi Fujimoto, Koichi Ito, Kai Wu, Kazunori Sato, Yasuyuki Taki, Hiroshi Fukuda, Takafumi Aoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

Estimating human age from brain MR images is useful for early detection of Alzheimer's disease. In this paper we propose a fast and accurate method based on deep learning to predict subject's age. Compared with previous methods, our algorithm achieves comparable accuracy using fewer input images. With our GPU version program, the time needed to make a prediction is 20 ms. We evaluate our methods using mean absolute error (MAE) and our method is able to predict subject's age with MAE of 4.0 years.

Original languageEnglish
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Pages849-852
Number of pages4
ISBN (Electronic)9781509011711
DOIs
Publication statusPublished - 2017 Jun 15
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: 2017 Apr 182017 Apr 21

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
CountryAustralia
CityMelbourne
Period17/4/1817/4/21

Keywords

  • Age estimation
  • Brain-aging
  • Deep learning
  • MRI
  • T1-weighted image

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Huang, T. W., Chen, H. T., Fujimoto, R., Ito, K., Wu, K., Sato, K., Taki, Y., Fukuda, H., & Aoki, T. (2017). Age estimation from brain MRI images using deep learning. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 (pp. 849-852). [7950650] (Proceedings - International Symposium on Biomedical Imaging). IEEE Computer Society. https://doi.org/10.1109/ISBI.2017.7950650