Automated Age Estimation from Hand MRI Volumes Using Deep Learning

Darko Stern, Christian Payer, Vincent Lepetit, Martin Urschler

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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±0.300.36±0.30 years, which is in line with radiologists doing visual BA estimation.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2016
Subtitle of host publication19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II
EditorsSebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells
PublisherSpringer International Publishing AG
Number of pages9
ISBN (Electronic)978-3-319-46723-8
ISBN (Print)978-3-319-46722-1
Publication statusPublished - 21 Oct 2016
Event19th International Conference on Medical Image Computing & Computer Assisted Intervention: MICCAI 2016 - Intercontinental Athenaeum, Athens, Greece
Duration: 17 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science


Conference19th International Conference on Medical Image Computing & Computer Assisted Intervention
Abbreviated titleMICCAI
Internet address

Fields of Expertise

  • Information, Communication & Computing


  • BioTechMed-Graz

Cite this