DisguisOR: Holistic Face Anonymization for the Operating Room
Lennart Bastian Tony Danjun Wang Tobias Czempiel Benjamin Busam Nassir Navab
Purpose: Recent advances in Surgical Data Science (SDS) have contributed to
an increase in video recordings from hospital environments. While methods such
as surgical workflow recognition show potential in increasing the quality of
patient care, the quantity of video data has surpassed the scale at which
images can be manually anonymized. Existing automated 2D anonymization methods
under-perform in Operating Rooms (OR), due to occlusions and obstructions. We
propose to anonymize multi-view OR recordings using 3D data from multiple
camera streams. Methods: RGB and depth images from multiple cameras are fused
into a 3D point cloud representation of the scene. We then detect each
individual's face in 3D by regressing a parametric human mesh model onto
detected 3D human keypoints and aligning the face mesh with the fused 3D point
cloud. The mesh model is rendered into every acquired camera view, replacing
each individual's face. Results: Our method shows promise in locating faces at
a higher rate than existing approaches. DisguisOR produces geometrically
consistent anonymizations for each camera view, enabling more realistic
anonymization that is less detrimental to downstream tasks. Conclusion:
Frequent obstructions and crowding in operating rooms leaves significant room
for improvement for off-the-shelf anonymization methods. DisguisOR addresses
privacy on a scene level and has the potential to facilitate further research
in SDS.