Automated Age Estimation from Hand MRI Volumes Using Deep Learning

Darko Stern, Christian Payer, Vincent Lepetit, Martin Urschler

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Buch/BerichtBegutachtung

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.
Originalspracheenglisch
TitelMedical Image Computing and Computer-Assisted Intervention – MICCAI 2016
Untertitel19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II
Redakteure/-innenSebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells
Herausgeber (Verlag)Springer International Publishing AG
Seiten194-202
Seitenumfang9
Band9901
ISBN (elektronisch)978-3-319-46723-8
ISBN (Print)978-3-319-46722-1
DOIs
PublikationsstatusVeröffentlicht - 21 Okt 2016
Veranstaltung19th International Conference on Medical Image Computing & Computer Assisted Intervention: MICCAI 2016 - Intercontinental Athenaeum, Athens, Griechenland
Dauer: 17 Okt 201621 Okt 2016
http://www.miccai2016.org

Publikationsreihe

NameLecture Notes in Computer Science
Herausgeber (Verlag)Springer

Konferenz

Konferenz19th International Conference on Medical Image Computing & Computer Assisted Intervention
KurztitelMICCAI
LandGriechenland
OrtAthens
Zeitraum17/10/1621/10/16
Internetadresse

Fields of Expertise

  • Information, Communication & Computing

Kooperationen

  • BioTechMed-Graz

Fingerprint

Untersuchen Sie die Forschungsthemen von „Automated Age Estimation from Hand MRI Volumes Using Deep Learning“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren