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Automated Age Estimation from Hand MRI Volumes Using Deep Learning

  • Darko ŠternEmail author
  • Christian Payer
  • Vincent Lepetit
  • Martin Urschler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

Abstract

Biological age (BA) estimation from radiologic data is an important topic in clinical medicine, e.g. in determining endocrinological diseases or planning paediatric orthopaedic surgeries, while in legal medicine it is employed to approximate chronological age. In this work, we propose the use of deep convolutional neural networks (DCNN) for automatic BA estimation from hand MRI volumes, inspired by the way radiologists visually perform age estimation using established staging schemes that follow physical maturation. In our results we outperform the state of the art automatic BA estimation method, achieving a mean error between estimated and ground truth BA of \(0.36\,\pm \,0.30\) years, which is in line with radiologists doing visual BA estimation.

Keywords

Epiphyseal Plate Stochastic Gradient Descent Intensity Image Hand Bone Deep Convolutional Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Darko Štern
    • 1
    Email author
  • Christian Payer
    • 2
  • Vincent Lepetit
    • 2
  • Martin Urschler
    • 1
    • 2
    • 3
  1. 1.Ludwig Boltzmann Institute for Clinical Forensic ImagingGrazAustria
  2. 2.Institute for Computer Graphics and VisionGraz University of TechnologyGrazAustria
  3. 3.BioTechMed-GrazGrazAustria

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