DisguisOR: holistic face anonymization for the operating room.

ABSTRACT:

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

SUBMITTER: Bastian L 

PROVIDER: S-EPMC10329611 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature