Smoke Classification in Laparoscopic Cholecystectomy Videos Incorporating Spatio-temporal Information

  • 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.

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Metadaten
Author:Tobias Rueckert, Maximilian Rieder, Hubertus Feussner, Dirk WilhelmORCiD, Daniel RueckertORCiD, Christoph PalmORCiD
DOI:https://doi.org/10.1007/978-3-658-44037-4_78
Parent Title (German):Bildverarbeitung für die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen
Publisher:Springeer
Place of publication:Wiesbaden
Editor:Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2024
Release Date:2024/03/22
First Page:298
Last Page:303
Andere Schriftenreihe:Informatik aktuell
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
Begutachtungsstatus:begutachtet
Kostenträger (Forschungsprojekt, Labor, Einrichtung etc.):2027701
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG