@inproceedings{ScheyttWiemuthBurgert2017,
  author    = {Scheytt, Josia and Wiemuth, Markus and Burgert, Oliver},
  title     = {Binary endoscope localization from laparoscopic video as virtual sensor for automatic workflow detection},
  series = {CURAC 2017 - Tagungsband : 16. Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC) : 5.-7. Oktober 2017, Hannover},
  booktitle = {CURAC 2017 - Tagungsband : 16. Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC) : 5.-7. Oktober 2017, Hannover},
  editor    = {Burgner-Kahrs, Jessica},
  publisher = {PZH Verlag, TEWISS-Technik und Wissen GmbH},
  address   = {Garbsen},
  isbn      = {978-3-95900-158-8},
  pages     = {191 -- 196},
  year      = {2017},
  abstract  = {This paper contributes to the automatic detection of perioperative workflow by developing a binary endoscope localization. Automated situation recognition in the context of an intelligent operating room requires the automatic conversion of low level cues into more abstract high level information. Imagery from a laparoscope delivers rich content that is easy to obtain but hard to process. We introduce a system which detects if the endoscope's distal tip is inside or outsiede the patient based on the endoscope video. This information can be used as one parameter in a situation recognition pipeline. Our localization performs in real-time at a video resolution of 1280x720 and 5-fold cross validation yields mean F1-scores of up to 0,94 on videos of 7 laparoscopies.},
  language  = {en}
}