An age estimation method using 3d-cnn from brain MRI images

Masaru Ueda, Koichi Ito, Kai Wu, Kazunori Sato, Yasuyuki Taki, Hiroshi Fukuda, Takafumi Aoki

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

10 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages380-383
Number of pages4
ISBN (Electronic)9781538636411
DOIs
Publication statusPublished - 2019 Apr
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 2019 Apr 82019 Apr 11

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2019-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/TerritoryItaly
CityVenice
Period19/4/819/4/11

Keywords

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

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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