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Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.

CiteScore 2018: 0.47

Source Normalized Impact per Paper (SNIP) 2018: 0.377

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Automated detection of bone splinters in DEXA phantoms using deep neural networks

Steffen Rüger / Markus Firsching / Julija Lucic / Alexander Ennen / Norman Uhlmann / Thomas Wittenberg
  • Fraunhofer Institute for Integrated Circuits IIS & University Erlangen, Am Wolfsmantel 33, Erlangen, Germany
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Published Online: 2019-09-18 | DOI: https://doi.org/10.1515/cdbme-2019-0071


Dual energy radiographic imaging is a method to provide material information and can be used to differentiate between various tissue types. Dual energy X-ray absorption (DEXA) can be applied for breast density, osteoporosis or bone fracture analysis. To support radiologists with the assessment of DEXA images, machine learning can be applied. Specifically, deep convolutional neural networks (DCNNs) can be used for medical image analysis. In this work a DCNN is proposed and evaluated for automated detection of bone splinters in DEXA phantom images. The image data consists of 47 phantoms with (35) and without (12) bone splinters. Material decomposition and energy weighting results in additional image channels. Various DCNN architectures and parameters were explored. A classification rate in regions with 90 % and without 99 % bone splinters was achieved.

Keywords: DEXA; CAD; DCNN; dual energy

About the article

Published Online: 2019-09-18

Published in Print: 2019-09-01

Citation Information: Current Directions in Biomedical Engineering, Volume 5, Issue 1, Pages 281–283, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2019-0071.

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© 2019 by Walter de Gruyter Berlin/Boston. This work is licensed under the Creative Commons Attribution 4.0 Public License. BY 4.0

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