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@ARTICLE{StudierFischer:180692,
      author       = {A. Studier-Fischer and S. Seidlitz$^*$ and J. Sellner$^*$
                      and B. Özdemir and M. Wiesenfarth$^*$ and L. Ayala$^*$ and
                      J. Odenthal and S. Knödler and K. F. Kowalewski and C. M.
                      Haney and I. Camplisson and M. Dietrich and K. Schmidt and
                      G. A. Salg and H. G. Kenngott and T. Adler$^*$ and N.
                      Schreck$^*$ and A. Kopp-Schneider$^*$ and K. Maier-Hein$^*$
                      and L. Maier-Hein$^*$ and B. P. Müller-Stich and F. Nickel},
      title        = {{S}pectral organ fingerprints for machine learning-based
                      intraoperative tissue classification with hyperspectral
                      imaging in a porcine model.},
      journal      = {Scientific reports},
      volume       = {12},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {DKFZ-2022-01488},
      pages        = {11028},
      year         = {2022},
      abstract     = {Visual discrimination of tissue during surgery can be
                      challenging since different tissues appear similar to the
                      human eye. Hyperspectral imaging (HSI) removes this
                      limitation by associating each pixel with high-dimensional
                      spectral information. While previous work has shown its
                      general potential to discriminate tissue, clinical
                      translation has been limited due to the method's current
                      lack of robustness and generalizability. Specifically, the
                      scientific community is lacking a comprehensive spectral
                      tissue atlas, and it is unknown whether variability in
                      spectral reflectance is primarily explained by tissue type
                      rather than the recorded individual or specific acquisition
                      conditions. The contribution of this work is threefold: (1)
                      Based on an annotated medical HSI data set (9059 images from
                      46 pigs), we present a tissue atlas featuring spectral
                      fingerprints of 20 different porcine organs and tissue
                      types. (2) Using the principle of mixed model analysis, we
                      show that the greatest source of variability related to HSI
                      images is the organ under observation. (3) We show that
                      HSI-based fully-automatic tissue differentiation of 20 organ
                      classes with deep neural networks is possible with high
                      accuracy (> $95\%).$ We conclude from our study that
                      automatic tissue discrimination based on HSI data is
                      feasible and could thus aid in intraoperative decisionmaking
                      and pave the way for context-aware computer-assisted surgery
                      systems and autonomous robotics.},
      keywords     = {Animals / Hyperspectral Imaging / Machine Learning / Neural
                      Networks, Computer / Swine},
      cin          = {E130 / E230 / C060},
      ddc          = {600},
      cid          = {I:(DE-He78)E130-20160331 / I:(DE-He78)E230-20160331 /
                      I:(DE-He78)C060-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35773276},
      pmc          = {pmc:PMC9247052},
      doi          = {10.1038/s41598-022-15040-w},
      url          = {https://inrepo02.dkfz.de/record/180692},
}