Automated Age Estimation from Hand MRI Volumes Using Deep Learning

Darko Stern; Christian Payer; Vincent Lepetit; Martin Urschler

doi:10.1007/978-3-319-46723-8_23

Automated Age Estimation from Hand MRI Volumes Using Deep Learning

Darko Stern, Christian Payer, Vincent Lepetit, Martin Urschler

Institute of Computer Graphics and Vision (7100)

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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±0.300.36±0.30 years, which is in line with radiologists doing visual BA estimation.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
Subtitle of host publication	19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II
Editors	Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells
Publisher	Springer International Publishing AG
Pages	194-202
Number of pages	9
Volume	9901
ISBN (Electronic)	978-3-319-46723-8
ISBN (Print)	978-3-319-46722-1
DOIs	https://doi.org/10.1007/978-3-319-46723-8_23
Publication status	Published - 21 Oct 2016
Event	19th International Conference on Medical Image Computing & Computer Assisted Intervention: MICCAI 2016 - Intercontinental Athenaeum, Athens, Greece Duration: 17 Oct 2016 → 21 Oct 2016 http://www.miccai2016.org

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer

Conference

Conference	19th International Conference on Medical Image Computing & Computer Assisted Intervention
Abbreviated title	MICCAI
Country/Territory	Greece
City	Athens
Period	17/10/16 → 21/10/16
Internet address	http://www.miccai2016.org

Fields of Expertise

Information, Communication & Computing

Cooperations

BioTechMed-Graz

Access to Document

10.1007/978-3-319-46723-8_23

FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents
Bischof, H. & Urschler, M.
1/07/15 → 31/12/18
Project: Research project
Klinisch-Forensische Bildgebung - Developing methods to standardize computer-assisted medical MRI datas [Original in Deutsch: Klinisch-Forensische Bildgebung] - Standard_MRT
Urschler, M., Bornik, A. & Bischof, H.
1/12/08 → 31/05/15
Project: Research project

Cite this

Stern, D., Payer, C., Lepetit, V., & Urschler, M. (2016). Automated Age Estimation from Hand MRI Volumes Using Deep Learning. In S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, & W. Wells (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II (Vol. 9901, pp. 194-202). (Lecture Notes in Computer Science). Springer International Publishing AG . https://doi.org/10.1007/978-3-319-46723-8_23

Automated Age Estimation from Hand MRI Volumes Using Deep Learning. / Stern, Darko; Payer, Christian; Lepetit, Vincent et al.
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II. ed. / Sebastien Ourselin; Leo Joskowicz; Mert R. Sabuncu; Gozde Unal; William Wells. Vol. 9901 Springer International Publishing AG , 2016. p. 194-202 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Stern, D, Payer, C, Lepetit, V & Urschler, M 2016, Automated Age Estimation from Hand MRI Volumes Using Deep Learning. in S Ourselin, L Joskowicz, MR Sabuncu, G Unal & W Wells (eds), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II. vol. 9901, Lecture Notes in Computer Science, Springer International Publishing AG , pp. 194-202, 19th International Conference on Medical Image Computing & Computer Assisted Intervention, Athens, Greece, 17/10/16. https://doi.org/10.1007/978-3-319-46723-8_23

Stern D, Payer C, Lepetit V, Urschler M. Automated Age Estimation from Hand MRI Volumes Using Deep Learning. In Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W, editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II. Vol. 9901. Springer International Publishing AG . 2016. p. 194-202. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-46723-8_23

Stern, Darko ; Payer, Christian ; Lepetit, Vincent et al. / Automated Age Estimation from Hand MRI Volumes Using Deep Learning. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II. editor / Sebastien Ourselin ; Leo Joskowicz ; Mert R. Sabuncu ; Gozde Unal ; William Wells. Vol. 9901 Springer International Publishing AG , 2016. pp. 194-202 (Lecture Notes in Computer Science).

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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±0.300.36±0.30 years, which is in line with radiologists doing visual BA estimation.",

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Automated Age Estimation from Hand MRI Volumes Using Deep Learning

Abstract

Publication series

Conference

Fields of Expertise

Cooperations

Access to Document

Fingerprint

Projects

FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents

Klinisch-Forensische Bildgebung - Developing methods to standardize computer-assisted medical MRI datas [Original in Deutsch: Klinisch-Forensische Bildgebung] - Standard_MRT

Cite this