Multi-factorial age estimation from skeletal and dental MRI volumes

Darko Štern; Philipp Kainz; Christian Payer; Martin Urschler

doi:10.1007/978-3-319-67389-9_8

Multi-factorial age estimation from skeletal and dental MRI volumes

Darko Štern, Philipp Kainz, Christian Payer, Martin Urschler^*

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Maschinelles Sehen und Darstellen (7100)

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

Abstract

Age estimation from radiologic data is an important topic in forensic medicine to assess chronological age or to discriminate minors from adults, e.g. asylum seekers lacking valid identification documents. In this work we propose automatic multi-factorial age estimation methods based on MRI data to extend the maximal age range from 19 years, as commonly used for age assessment based on hand bones, up to 25 years, when combined with wisdom teeth and clavicles. Mimicking how radiologists perform age estimation, our proposed method based on deep convolutional neural networks achieves a result of 1.14 \pm 0.96 years of mean absolute error in predicting chronological age. Further, when fine-tuning the same network for majority age classification, we show an improvement in sensitivity of the multi-factorial system compared to solely relying on the hand.

Originalsprache	englisch
Titel	Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
Herausgeber (Verlag)	Springer-Verlag Italia
Seiten	61-69
Seitenumfang	9
Band	10541 LNCS
ISBN (Print)	9783319673882
DOIs	https://doi.org/10.1007/978-3-319-67389-9_8
Publikationsstatus	Veröffentlicht - 2017
Veranstaltung	8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017: MLMI 2017 / MICCAI 2017 - Quebec City, Kanada Dauer: 10 Sept. 2017 → 10 Sept. 2017

Publikationsreihe

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band	10541 LNCS
ISSN (Print)	03029743
ISSN (elektronisch)	16113349

Konferenz

Konferenz	8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Land/Gebiet	Kanada
Ort	Quebec City
Zeitraum	10/09/17 → 10/09/17

ASJC Scopus subject areas

Theoretische Informatik
Informatik (insg.)

Fields of Expertise

Information, Communication & Computing

Kooperationen

BioTechMed-Graz

Zugriff auf Dokument

10.1007/978-3-319-67389-9_8

Andere Dateien und Links

http://www.scopus.com/inward/record.url?scp=85029716189&partnerID=8YFLogxK

FWF - FAME - Vollautomatische MRT-basierte Altersschätzung von Jugendlichen
Bischof, H. & Urschler, M.
1/07/15 → 31/12/18
Projekt: Forschungsprojekt

Dieses zitieren

Štern, D., Kainz, P., Payer, C., & Urschler, M. (2017). Multi-factorial age estimation from skeletal and dental MRI volumes. in Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings (Band 10541 LNCS, S. 61-69). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10541 LNCS). Springer-Verlag Italia. https://doi.org/10.1007/978-3-319-67389-9_8

Multi-factorial age estimation from skeletal and dental MRI volumes. / Štern, Darko; Kainz, Philipp; Payer, Christian et al.
Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Band 10541 LNCS Springer-Verlag Italia, 2017. S. 61-69 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10541 LNCS).

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

Štern, D, Kainz, P, Payer, C & Urschler, M 2017, Multi-factorial age estimation from skeletal and dental MRI volumes. in Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Bd. 10541 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 10541 LNCS, Springer-Verlag Italia, S. 61-69, 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Kanada, 10/09/17. https://doi.org/10.1007/978-3-319-67389-9_8

Štern D, Kainz P, Payer C, Urschler M. Multi-factorial age estimation from skeletal and dental MRI volumes. in Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Band 10541 LNCS. Springer-Verlag Italia. 2017. S. 61-69. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67389-9_8

Štern, Darko ; Kainz, Philipp ; Payer, Christian et al. / Multi-factorial age estimation from skeletal and dental MRI volumes. Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Band 10541 LNCS Springer-Verlag Italia, 2017. S. 61-69 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Multi-factorial age estimation from skeletal and dental MRI volumes",

abstract = "Age estimation from radiologic data is an important topic in forensic medicine to assess chronological age or to discriminate minors from adults, e.g. asylum seekers lacking valid identification documents. In this work we propose automatic multi-factorial age estimation methods based on MRI data to extend the maximal age range from 19 years, as commonly used for age assessment based on hand bones, up to 25 years, when combined with wisdom teeth and clavicles. Mimicking how radiologists perform age estimation, our proposed method based on deep convolutional neural networks achieves a result of 1.14 \pm 0.96 years of mean absolute error in predicting chronological age. Further, when fine-tuning the same network for majority age classification, we show an improvement in sensitivity of the multi-factorial system compared to solely relying on the hand.",

keywords = "Convolutional neural network, Forensic age estimation, Information fusion, Multi-factorial method, Random forest",

author = "Darko {\v S}tern and Philipp Kainz and Christian Payer and Martin Urschler",

year = "2017",

doi = "10.1007/978-3-319-67389-9_8",

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AU - Štern, Darko

AU - Kainz, Philipp

AU - Payer, Christian

AU - Urschler, Martin

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Y1 - 2017

N2 - Age estimation from radiologic data is an important topic in forensic medicine to assess chronological age or to discriminate minors from adults, e.g. asylum seekers lacking valid identification documents. In this work we propose automatic multi-factorial age estimation methods based on MRI data to extend the maximal age range from 19 years, as commonly used for age assessment based on hand bones, up to 25 years, when combined with wisdom teeth and clavicles. Mimicking how radiologists perform age estimation, our proposed method based on deep convolutional neural networks achieves a result of 1.14 \pm 0.96 years of mean absolute error in predicting chronological age. Further, when fine-tuning the same network for majority age classification, we show an improvement in sensitivity of the multi-factorial system compared to solely relying on the hand.

AB - Age estimation from radiologic data is an important topic in forensic medicine to assess chronological age or to discriminate minors from adults, e.g. asylum seekers lacking valid identification documents. In this work we propose automatic multi-factorial age estimation methods based on MRI data to extend the maximal age range from 19 years, as commonly used for age assessment based on hand bones, up to 25 years, when combined with wisdom teeth and clavicles. Mimicking how radiologists perform age estimation, our proposed method based on deep convolutional neural networks achieves a result of 1.14 \pm 0.96 years of mean absolute error in predicting chronological age. Further, when fine-tuning the same network for majority age classification, we show an improvement in sensitivity of the multi-factorial system compared to solely relying on the hand.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

Multi-factorial age estimation from skeletal and dental MRI volumes

Abstract

Publikationsreihe

Konferenz

ASJC Scopus subject areas

Fields of Expertise

Kooperationen

Zugriff auf Dokument

Andere Dateien und Links

Fingerprint

Projekte

FWF - FAME - Vollautomatische MRT-basierte Altersschätzung von Jugendlichen

Dieses zitieren