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.

翻译：目的：近期手术数据科学领域的进展导致医院环境视频录制数量激增。尽管手术工作流识别等方法在提升患者护理质量方面展现出潜力，但视频数据量已远超人工图像匿名化的处理规模。现有二维自动匿名化方法因遮挡与阻塞问题，在手术室环境中表现欠佳。我们提出利用多视角手术室视频流的三维数据进行匿名化处理。方法：将多台摄像机的RGB与深度图像融合为场景三维点云表示，通过将参数化人体网格模型回归至检测到的三维人体关键点，并将面部网格与融合后的三维点云对齐，实现每位个体面部的三维检测。将网格模型渲染至每个摄像头视角中，替换每位个体的面部。结果：本方法在面部定位率上优于现有方法。DisguisOR可为每个摄像机视角生成几何一致的匿名化结果，实现更逼真且对下游任务损害更小的匿名化效果。结论：手术室中频繁出现的遮挡与拥挤状况，使得现有匿名化方法仍有显著改进空间。DisguisOR在场景层面解决隐私问题，有望推动手术数据科学领域的进一步研究。