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

Institut für Maschinelles Sehen und Darstellen (7100)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Buch/Bericht › Begutachtung

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.

Originalsprache	englisch
Titel	Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
Untertitel	19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II
Redakteure/-innen	Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells
Herausgeber (Verlag)	Springer International Publishing AG
Seiten	194-202
Seitenumfang	9
Band	9901
ISBN (elektronisch)	978-3-319-46723-8
ISBN (Print)	978-3-319-46722-1
DOIs	https://doi.org/10.1007/978-3-319-46723-8_23
Publikationsstatus	Veröffentlicht - 21 Okt. 2016
Veranstaltung	19th International Conference on Medical Image Computing & Computer Assisted Intervention: MICCAI 2016 - Intercontinental Athenaeum, Athens, Griechenland Dauer: 17 Okt. 2016 → 21 Okt. 2016 http://www.miccai2016.org

Publikationsreihe

Name	Lecture Notes in Computer Science
Herausgeber (Verlag)	Springer

Konferenz

Konferenz	19th International Conference on Medical Image Computing & Computer Assisted Intervention
Kurztitel	MICCAI
Land/Gebiet	Griechenland
Ort	Athens
Zeitraum	17/10/16 → 21/10/16
Internetadresse	http://www.miccai2016.org

Fields of Expertise

Information, Communication & Computing

Kooperationen

BioTechMed-Graz

Zugriff auf Dokument

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

FWF - FAME - Vollautomatische MRT-basierte Altersschätzung von Jugendlichen
Bischof, H. & Urschler, M.
1/07/15 → 31/12/18
Projekt: Forschungsprojekt
Klinisch-Forensische Bildgebung- Standardisierung zur computerunterstützten Auswertung von medizinischen MRT-Daten - Standard_MRT
Urschler, M., Bornik, A. & Bischof, H.
1/12/08 → 31/12/15
Projekt: Forschungsprojekt

Dieses zitieren

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 (Hrsg.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II (Band 9901, S. 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. Hrsg. / Sebastien Ourselin; Leo Joskowicz; Mert R. Sabuncu; Gozde Unal; William Wells. Band 9901 Springer International Publishing AG , 2016. S. 194-202 (Lecture Notes in Computer Science).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Buch/Bericht › Begutachtung

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 (Hrsg.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II. Bd. 9901, Lecture Notes in Computer Science, Springer International Publishing AG , S. 194-202, 19th International Conference on Medical Image Computing & Computer Assisted Intervention, Athens, Griechenland, 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, Hrsg., Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II. Band 9901. Springer International Publishing AG . 2016. S. 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. Hrsg. / Sebastien Ourselin ; Leo Joskowicz ; Mert R. Sabuncu ; Gozde Unal ; William Wells. Band 9901 Springer International Publishing AG , 2016. S. 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

Publikationsreihe

Konferenz

Fields of Expertise

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Projekte

FWF - FAME - Vollautomatische MRT-basierte Altersschätzung von Jugendlichen

Klinisch-Forensische Bildgebung- Standardisierung zur computerunterstützten Auswertung von medizinischen MRT-Daten - Standard_MRT

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