Advertisement

Automatic Smoke Classification in Endoscopic Video

  • Andreas LeibetsederEmail author
  • Manfred Jürgen Primus
  • Klaus Schoeffmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10705)

Abstract

Medical smoke evacuation systems enable proper, filtered removal of toxic fumes during surgery, while stabilizing internal pressure during endoscopic interventions. Typically activated manually, they, however, are prone to inefficient utilization: tardy activation enables smoke to interfere with ongoing surgeries and late deactivation wastes precious resources. In order to address such issues, in this work we demonstrate a vision-based tool indicating endoscopic smoke – a first step towards automatic activation of said systems and avoiding human misconduct. In the back-end we employ a pre-trained convolutional neural network (CNN) model for distinguishing images containing smoke from others.

Keywords

Endoscopy Smoke detection User interface 

Notes

Acknowledgements

This work was supported by Universität Klagenfurt and Lakeside Labs GmbH, Klagenfurt, Austria and funding from the European Regional Development Fund and the Carinthian Economic Promotion Fund (KWF) under grant KWF 20214 u. 3520/26336/38165.

References

  1. 1.
    Leibetseder, A., Primus, M.J., Petscharnig, S., Schoeffmann, K.: Image-based smoke detection in laparoscopic videos. In: Cardoso, M.J., et al. (eds.) CARE/CLIP -2017. LNCS, vol. 10550, pp. 70–87. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67543-5_7 CrossRefGoogle Scholar
  2. 2.
    Leibetseder, A., Primus, M.J., Petscharnig, S., Schoeffmann, K.: Real-time image-based smoke detection in endoscopic videos. In: ACM Multimedia Conference on Multimedia 2017 - Thematic Workshops, Mountain View, CA, USA (2017)Google Scholar
  3. 3.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Andreas Leibetseder
    • 1
    Email author
  • Manfred Jürgen Primus
    • 1
  • Klaus Schoeffmann
    • 1
  1. 1.Institute of Information TechnologyAlpen-Adria UniversityKlagenfurtAustria

Personalised recommendations