% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Wirkert:130948,
      author       = {S. Wirkert$^*$ and H. Kenngott and B. Mayer and P.
                      Mietkowski and M. Wagner and P. Sauer and N. T. Clancy and
                      D. S. Elson and L. Maier-Hein$^*$},
      title        = {{R}obust near real-time estimation of physiological
                      parameters from megapixel multispectral images with inverse
                      {M}onte {C}arlo and random forest regression.},
      journal      = {International journal of computer assisted radiology and
                      surgery},
      volume       = {11},
      number       = {6},
      issn         = {1861-6429},
      address      = {Berlin},
      publisher    = {Springer},
      reportid     = {DKFZ-2017-06024},
      pages        = {909 - 917},
      year         = {2016},
      abstract     = {Multispectral imaging can provide reflectance measurements
                      at multiple spectral bands for each image pixel. These
                      measurements can be used for estimation of important
                      physiological parameters, such as oxygenation, which can
                      provide indicators for the success of surgical treatment or
                      the presence of abnormal tissue. The goal of this work was
                      to develop a method to estimate physiological parameters in
                      an accurate and rapid manner suited for modern
                      high-resolution laparoscopic images.While previous methods
                      for oxygenation estimation are based on either simple linear
                      methods or complex model-based approaches exclusively suited
                      for off-line processing, we propose a new approach that
                      combines the high accuracy of model-based approaches with
                      the speed and robustness of modern machine learning methods.
                      Our concept is based on training random forest regressors
                      using reflectance spectra generated with Monte Carlo
                      simulations.According to extensive in silico and in vivo
                      experiments, the method features higher accuracy and
                      robustness than state-of-the-art online methods and is
                      orders of magnitude faster than other nonlinear regression
                      based methods.Our current implementation allows for near
                      real-time oxygenation estimation from megapixel
                      multispectral images and is thus well suited for online
                      tissue analysis.},
      keywords     = {Hemoglobins (NLM Chemicals) / Oxygen (NLM Chemicals)},
      cin          = {E131},
      ddc          = {610},
      cid          = {I:(DE-He78)E131-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:27142459},
      pmc          = {pmc:PMC4893375},
      doi          = {10.1007/s11548-016-1376-5},
      url          = {https://inrepo02.dkfz.de/record/130948},
}