Multi-factorial Age Estimation from Skeletal and Dental MRI Volumes View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Darko Štern , Philipp Kainz , Christian Payer , Martin Urschler

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. More... »

PAGES

61-69

References to SciGraph publications

  • 2001-10. Random Forests in MACHINE LEARNING
  • 2016. Regressing Heatmaps for Multiple Landmark Localization Using CNNs in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • 2016. Automated Age Estimation from Hand MRI Volumes Using Deep Learning in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • 2004-02. Studies on the time frame for ossification of the medial clavicular epiphyseal cartilage in conventional radiography in INTERNATIONAL JOURNAL OF LEGAL MEDICINE
  • Book

    TITLE

    Machine Learning in Medical Imaging

    ISBN

    978-3-319-67388-2
    978-3-319-67389-9

    Author Affiliations

    From Grant

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-67389-9_8

    DOI

    http://dx.doi.org/10.1007/978-3-319-67389-9_8

    DIMENSIONS

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