Image-Based Smoke Detection in Laparoscopic Videos

  • Andreas LeibetsederEmail author
  • Manfred Jürgen Primus
  • Stefan Petscharnig
  • Klaus Schoeffmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10550)


The development and improper removal of smoke during minimally invasive surgery (MIS) can considerably impede a patient’s treatment, while additionally entailing serious deleterious health effects. Hence, state-of-the-art surgical procedures employ smoke evacuation systems, which often still are activated manually by the medical staff or less commonly operate automatically utilizing industrial, highly-specialized and operating room (OR) approved sensors. As an alternate approach, video analysis can be used to take on said detection process – a topic not yet much researched in aforementioned context. In order to advance in this sector, we propose utilizing an image-based smoke classification task on a pre-trained convolutional neural network (CNN). We provide a custom data set of over 30 000 laparoscopic smoke/non-smoke images, part of which served as training data for GoogLeNet-based [41] CNN models. To be able to compare our research for evaluation, we separately developed a non-CNN classifier based on observing the saturation channel of a sample picture in the HSV color space. While the deep learning approaches yield excellent results with Receiver Operating Characteristic (ROC) curves enclosing areas of over 0.98, the computationally much less costly analysis of an image’s saturation histogram under certain circumstances can, surprisingly, as well be a good indicator for smoke with areas under the curves (AUCs) of around 0.92–0.97.


Smoke detection Endoscopy Image processing Deep learning 



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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

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