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DisguisOR: holistic face anonymization for the operating room.

Lennart Bastian1, Tony Danjun Wang2, Tobias Czempiel2

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This study introduces DisguisOR, a novel 3D approach for anonymizing faces in surgical videos, overcoming limitations of 2D methods in operating rooms. It enhances privacy and supports surgical data science research.

Keywords:
AnonymizationFace detectionMulti-view operating roomsSurgical data scienceSurgical workflow recognition

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Area of Science:

  • Surgical Data Science (SDS)
  • Computer Vision
  • Medical Imaging

Background:

  • The increasing volume of surgical videos presents challenges for manual data anonymization.
  • Existing 2D anonymization techniques are insufficient for complex operating room (OR) environments due to occlusions.

Purpose of the Study:

  • To develop and evaluate a novel 3D method for anonymizing multi-view operating room recordings.
  • To address the limitations of current automated anonymization methods in cluttered OR settings.

Main Methods:

  • Fusion of multi-camera RGB and depth images into a 3D point cloud.
  • 3D face detection using parametric human mesh models and keypoint regression.
  • Rendering of anonymized 3D face meshes into original camera views.

Main Results:

  • The proposed method, DisguisOR, demonstrates improved face localization compared to existing approaches.
  • DisguisOR generates geometrically consistent and realistic anonymizations across multiple camera views.
  • Anonymizations are less detrimental to downstream data analysis tasks.

Conclusions:

  • DisguisOR offers a scene-level solution for privacy in crowded ORs, outperforming standard methods.
  • The technique has the potential to significantly advance research in Surgical Data Science by enabling the use of sensitive video data.