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@ARTICLE{MaierHein:168373,
      author       = {L. Maier-Hein$^*$ and M. Wagner and T. Ross$^*$ and A.
                      Reinke$^*$ and S. Bodenstedt and P. M. Full$^*$ and H.
                      Hempe$^*$ and D. Mindroc-Filimon$^*$ and P. Scholz$^*$ and
                      T. N. Tran$^*$ and P. Bruno$^*$ and A. Kisilenko and B.
                      Müller and T. Davitashvili and M. Capek and M. D.
                      Tizabi$^*$ and M. Eisenmann$^*$ and T. J. Adler$^*$ and J.
                      Gröhl$^*$ and M. Schellenberg$^*$ and S. Seidlitz$^*$ and
                      T. Y. E. Lai and B. Pekdemir$^*$ and V. Roethlingshoefer and
                      F. Both and S. Bittel and M. Mengler and L. Mündermann and
                      M. Apitz and A. Kopp-Schneider$^*$ and S. Speidel and F.
                      Nickel and P. Probst and H. G. Kenngott and B. P.
                      Müller-Stich},
      title        = {{H}eidelberg colorectal data set for surgical data science
                      in the sensor operating room.},
      journal      = {Scientific data},
      volume       = {8},
      number       = {1},
      issn         = {2052-4463},
      address      = {London},
      publisher    = {Nature Publ. Group},
      reportid     = {DKFZ-2021-00862},
      pages        = {101},
      year         = {2021},
      note         = {#EA:E130#},
      abstract     = {Image-based tracking of medical instruments is an integral
                      part of surgical data science applications. Previous
                      research has addressed the tasks of detecting, segmenting
                      and tracking medical instruments based on laparoscopic video
                      data. However, the proposed methods still tend to fail when
                      applied to challenging images and do not generalize well to
                      data they have not been trained on. This paper introduces
                      the Heidelberg Colorectal (HeiCo) data set - the first
                      publicly available data set enabling comprehensive
                      benchmarking of medical instrument detection and
                      segmentation algorithms with a specific emphasis on method
                      robustness and generalization capabilities. Our data set
                      comprises 30 laparoscopic videos and corresponding sensor
                      data from medical devices in the operating room for three
                      different types of laparoscopic surgery. Annotations include
                      surgical phase labels for all video frames as well as
                      information on instrument presence and corresponding
                      instance-wise segmentation masks for surgical instruments
                      (if any) in more than 10,000 individual frames. The data has
                      successfully been used to organize international
                      competitions within the Endoscopic Vision Challenges 2017
                      and 2019.},
      cin          = {E130 / C060},
      ddc          = {500},
      cid          = {I:(DE-He78)E130-20160331 / 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:33846356},
      doi          = {10.1038/s41597-021-00882-2},
      url          = {https://inrepo02.dkfz.de/record/168373},
}