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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

Wissenschaftlicher Beirat: 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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Deep convolutional neural network approach for forehead tissue thickness estimation

Jirapong Manit
  • Korrespondenzautor
  • Institute for Robotics and Cognitive Systems, Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, Germany
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/ Achim Schweikard / Floris Ernst
Online erschienen: 07.09.2017 | DOI: https://doi.org/10.1515/cdbme-2017-0022


In this paper, we presented a deep convolutional neural network (CNN) approach for forehead tissue thickness estimation. We use down sampled NIR laser backscattering images acquired from a novel marker-less near-infrared laser-based head tracking system, combined with the beam’s incident angle parameter. These two-channel augmented images were constructed for the CNN input, while a single node output layer represents the estimated value of the forehead tissue thickness. The models were – separately for each subject – trained and tested on datasets acquired from 30 subjects (high resolution MRI data is used as ground truth). To speed up training, we used a pre-trained network from the first subject to bootstrap training for each of the other subjects. We could show a clear improvement for the tissue thickness estimation (mean RMSE of 0.096 mm). This proposed CNN model outperformed previous support vector regression (mean RMSE of 0.155 mm) or Gaussian processes learning approaches (mean RMSE of 0.114 mm) and eliminated their restrictions for future research.

Keywords: Forehead Skin; Tissue Thickness; Deep Convolutional Neural Network; Regression; Head Tracking; Near-infrared Laser; Backscatter


Online erschienen: 07.09.2017

Quellenangabe: Current Directions in Biomedical Engineering, Band 3, Heft 2, Seiten 103–107, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2017-0022.

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©2017 Jirapong Manit et al., published by De Gruyter, Berlin/Boston. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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