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.
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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
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)
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)
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.
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Leibetseder, A., Primus, M.J., Schoeffmann, K. (2018). Automatic Smoke Classification in Endoscopic Video. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10705. Springer, Cham. https://doi.org/10.1007/978-3-319-73600-6_33
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DOI: https://doi.org/10.1007/978-3-319-73600-6_33
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