Deep Convolutional Neural Network Approach for Forehead Tissue Thickness Estimation from NIR Laser Backscattering Images

Abstract

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.
OriginalspracheEnglisch
ZeitschriftBiomedical Engineering
Jahrgang3
Ausgabenummer2
Seiten (von - bis)103–107
Seitenumfang5
ISSN2364-5504
DOIs
PublikationsstatusVeröffentlicht - 01.09.2017

Fingerprint

Untersuchen Sie die Forschungsthemen von „Deep Convolutional Neural Network Approach for Forehead Tissue Thickness Estimation from NIR Laser Backscattering Images“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitieren