@article{AhmedZeeshanDandekar2016,
  author    = {Ahmed, Zeeshan and Zeeshan, Saman and Dandekar, Thomas},
  title     = {Mining biomedical images towards valuable information retrieval in biomedical and life sciences},
  series = {Database - The Journal of Biological Databases and Curation},
  volume    = {2016},
  journal   = {Database - The Journal of Biological Databases and Curation},
  doi       = {10.1093/database/baw118},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-162697},
  pages     = {baw118},
  year      = {2016},
  abstract  = {Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.},
  language  = {en}
}
@article{PfitznerMayNuechter2018,
  author    = {Pfitzner, Christian and May, Stefan and N{\"u}chter, Andreas},
  title     = {Body weight estimation for dose-finding and health monitoring of lying, standing and walking patients based on RGB-D data},
  series = {Sensors},
  volume    = {18},
  journal   = {Sensors},
  number    = {5},
  doi       = {10.3390/s18051311},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176642},
  pages     = {1311},
  year      = {2018},
  abstract  = {This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients.},
  language  = {en}
}
@article{HerzStefanescuLohretal.2022,
  author    = {Herz, Stefan and Stefanescu, Maria R. and Lohr, David and Vogel, Patrick and Kosmala, Aleksander and Terekhov, Maxim and Weng, Andreas M. and Grunz, Jan-Peter and Bley, Thorsten A. and Schreiber, Laura M.},
  title     = {Effects of image homogeneity on stenosis visualization at 7 T in a coronary artery phantom study: With and without B1-shimming and parallel transmission},
  series = {PloS One},
  volume    = {17},
  journal   = {PloS One},
  number    = {6},
  doi       = {10.1371/journal.pone.0270689},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300129},
  year      = {2022},
  abstract  = {Background To investigate the effects of B\(_1\)-shimming and radiofrequency (RF) parallel transmission (pTX) on the visualization and quantification of the degree of stenosis in a coronary artery phantom using 7 Tesla (7 T) magnetic resonance imaging (MRI). Methods Stenosis phantoms with different grades of stenosis (0\%, 20\%, 40\%, 60\%, 80\%, and 100\%; 5 mm inner vessel diameter) were produced using 3D printing (clear resin). Phantoms were imaged with four different concentrations of diluted Gd-DOTA representing established arterial concentrations after intravenous injection in humans. Samples were centrally positioned in a thorax phantom of 30 cm diameter filled with a custom-made liquid featuring dielectric properties of muscle tissue. MRI was performed on a 7 T whole-body system. 2D-gradient-echo sequences were acquired with an 8-channel transmit 16-channel receive (8 Tx / 16 Rx) cardiac array prototype coil with and without pTX mode. Measurements were compared to those obtained with identical scan parameters using a commercially available 1 Tx / 16 Rx single transmit coil (sTX). To assess reproducibility, measurements (n = 15) were repeated at different horizontal angles with respect to the B0-field. Results B\(_1\)-shimming and pTX markedly improved flip angle homogeneity across the thorax phantom yielding a distinctly increased signal-to-noise ratio (SNR) averaged over a whole slice relative to non-manipulated RF fields. Images without B\(_1\)-shimming showed shading artifacts due to local B\(_1\)\(^+\)-field inhomogeneities, which hampered stenosis quantification in severe cases. In contrast, B\(_1\)-shimming and pTX provided superior image homogeneity. Compared with a conventional sTX coil higher grade stenoses (60\% and 80\%) were graded significantly (p<0.01) more precise. Mild to moderate grade stenoses did not show significant differences. Overall, SNR was distinctly higher with B\(_1\)-shimming and pTX than with the conventional sTX coil (inside the stenosis phantoms 14\%, outside the phantoms 32\%). Both full and half concentration (10.2 mM and 5.1 mM) of a conventional Gd-DOTA dose for humans were equally suitable for stenosis evaluation in this phantom study. Conclusions B\(_1\)-shimming and pTX at 7 T can distinctly improve image homogeneity and therefore provide considerably more accurate MR image analysis, which is beneficial for imaging of small vessel structures.},
  language  = {en}
}