@article{SeifarthGossmannGrosseetal.2015,
  author    = {Seifarth, Volker and Goßmann, Matthias and Grosse, J. O. and Becker, C. and Heschel, I. and Artmann, Gerhard and Temiz Artmann, Ayseg{\"u}l},
  title     = {Development of a Bioreactor to Culture Tissue Engineered Ureters Based on the Application of Tubular OPTIMAIX 3D Scaffolds},
  series = {Urologia Internationalis},
  volume    = {2015},
  journal   = {Urologia Internationalis},
  number    = {95},
  publisher = {Karger},
  address   = {Basel},
  issn      = {0042-1138},
  doi       = {10.1159/000368419},
  pages     = {106 -- 113},
  year      = {2015},
  language  = {en}
}
@article{ArinkinDigelPorstetal.2014,
  author    = {Arinkin, Vladimir and Digel, Ilya and Porst, Dariusz and Temiz Artmann, Ayseg{\"u}l and Artmann, Gerhard},
  title     = {Phenotyping date palm varieties via leaflet cross-sectional imaging and artificial neural network application},
  series = {BMC bioinformatics},
  volume    = {15},
  journal   = {BMC bioinformatics},
  number    = {55},
  issn      = {1471-2105},
  doi       = {10.1186/1471-2105-15-55},
  pages     = {1 -- 8},
  year      = {2014},
  abstract  = {Background True date palms (Phoenix dactylifera L.) are impressive trees and have served as an indispensable source of food for mankind in tropical and subtropical countries for centuries. The aim of this study is to differentiate date palm tree varieties by analysing leaflet cross sections with technical/optical methods and artificial neural networks (ANN). Results Fluorescence microscopy images of leaflet cross sections have been taken from a set of five date palm tree cultivars (Hewlat al Jouf, Khlas, Nabot Soltan, Shishi, Um Raheem). After features extraction from images, the obtained data have been fed in a multilayer perceptron ANN with backpropagation learning algorithm. Conclusions Overall, an accurate result in prediction and differentiation of date palm tree cultivars was achieved with average prediction in tenfold cross-validation is 89.1\% and reached 100\% in one of the best ANN.},
  language  = {en}
}
@article{MiciliValterOflazetal.2013,
  author    = {Micili, Serap C. and Valter, Markus and Oflaz, Hakan and Ozogul, Candan and Linder, Peter and F{\"o}ckler, Nicole and Artmann, Gerhard and Digel, Ilya and Temiz Artmann, Ayseg{\"u}l},
  title     = {Optical coherence tomography : a potential tool to predict premature rupture of fetal membranes},
  series = {Proceedings of the Institution of Mechanical Engineers. Part H : Journal of engineering in medicine},
  volume    = {Vol. 227},
  journal   = {Proceedings of the Institution of Mechanical Engineers. Part H : Journal of engineering in medicine},
  number    = {No. 4},
  publisher = {Sage},
  address   = {London},
  issn      = {0046-2039 (Print) ; 2041-3033 (E-Journal)},
  pages     = {393 -- 401},
  year      = {2013},
  language  = {en}
}