TRandAugment: temporal random augmentation strategy for surgical activity recognition from videos.

Journal: International journal of computer assisted radiology and surgery
Published Date:

Abstract

PURPOSE: Automatic recognition of surgical activities from intraoperative surgical videos is crucial for developing intelligent support systems for computer-assisted interventions. Current state-of-the-art recognition methods are based on deep learning where data augmentation has shown the potential to improve the generalization of these methods. This has spurred work on automated and simplified augmentation strategies for image classification and object detection on datasets of still images. Extending such augmentation methods to videos is not straightforward, as the temporal dimension needs to be considered. Furthermore, surgical videos pose additional challenges as they are composed of multiple, interconnected, and long-duration activities.

Authors

  • Sanat Ramesh
    Altair Robotics Lab, Department of Computer Science, University of Verona, Verona, Italy. sanat.ramesh@univr.it.
  • Diego Dall'Alba
    University of Verona, Verona, Italy.
  • Cristians Gonzalez
    University Hospital of Strasbourg, IHU Strasbourg, France.
  • Tong Yu
  • Pietro Mascagni
    IHU Strasbourg, Strasbourg, France.
  • Didier Mutter
    Institut Hospitalo-Universitaire, Institute of Image-Guided Surgery, University of Strasbourg, Fédération de Médecine Translationnelle de Strasbourg, Strasbourg, France3Department of Digestive Surgery, Strasbourg University Hospital, Fédération de Médecin.
  • Jacques Marescaux
  • Paolo Fiorini
    University of Verona, Verona, Italy.
  • Nicolas Padoy
    IHU Strasbourg, Strasbourg, France.