Smoke Classification in Laparoscopic Cholecystectomy Videos Incorporating Spatio-temporal Information

Tobias Rueckert, Maximilian Rieder, Hubertus Feussner, Dirk Wilhelm, Daniel Rueckert, Christoph Palm

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Heavy smoke development represents an important challenge for operating physicians during laparoscopic procedures and can potentially affect the success of an intervention due to reduced visibility and orientation. Reliable and accurate recognition of smoke is therefore a prerequisite for the use of downstream systems such as automated smoke evacuation systems. Current approaches distinguish between non-smoked and smoked frames but often ignore the temporal context inherent in endoscopic video data. In this work, we therefore present a method that utilizes the pixel-wise displacement from randomly sampled images to the preceding frames determined using the optical flow algorithm by providing the transformed magnitude of the displacement as an additional input to the network. Further, we incorporate the temporal context at evaluation time by applying an exponential moving average on the estimated class probabilities of the model output to obtain more stable and robust results over time. We evaluate our method on two convolutional-based and one state-of-the-art transformer architecture and show improvements in the classification results over a baseline approach, regardless of the network used.

Original languageEnglish
Title of host publicationBildverarbeitung für die Medizin 2024 - Proceedings, German Conference on Medical Image Computing, 2024
EditorsAndreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages6
ISBN (Print)9783658440367
StatePublished - 2024
EventGerman Conference on Medical Image Computing, BVM 2024 - Erlangen, Germany
Duration: 10 Mar 202412 Mar 2024

Publication series

NameInformatik aktuell
ISSN (Print)1431-472X


ConferenceGerman Conference on Medical Image Computing, BVM 2024


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