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DisguisOR: holistic face anonymization for the operating room

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...

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Autores principales: Bastian, Lennart, Wang, Tony Danjun, Czempiel, Tobias, Busam, Benjamin, Navab, Nassir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10329611/
https://www.ncbi.nlm.nih.gov/pubmed/37219807
http://dx.doi.org/10.1007/s11548-023-02939-6
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author Bastian, Lennart
Wang, Tony Danjun
Czempiel, Tobias
Busam, Benjamin
Navab, Nassir
author_facet Bastian, Lennart
Wang, Tony Danjun
Czempiel, Tobias
Busam, Benjamin
Navab, Nassir
author_sort Bastian, Lennart
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11548-023-02939-6.
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spelling pubmed-103296112023-07-10 DisguisOR: holistic face anonymization for the operating room Bastian, Lennart Wang, Tony Danjun Czempiel, Tobias Busam, Benjamin Navab, Nassir Int J Comput Assist Radiol Surg Original Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11548-023-02939-6. Springer International Publishing 2023-05-23 2023 /pmc/articles/PMC10329611/ /pubmed/37219807 http://dx.doi.org/10.1007/s11548-023-02939-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Bastian, Lennart
Wang, Tony Danjun
Czempiel, Tobias
Busam, Benjamin
Navab, Nassir
DisguisOR: holistic face anonymization for the operating room
title DisguisOR: holistic face anonymization for the operating room
title_full DisguisOR: holistic face anonymization for the operating room
title_fullStr DisguisOR: holistic face anonymization for the operating room
title_full_unstemmed DisguisOR: holistic face anonymization for the operating room
title_short DisguisOR: holistic face anonymization for the operating room
title_sort disguisor: holistic face anonymization for the operating room
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10329611/
https://www.ncbi.nlm.nih.gov/pubmed/37219807
http://dx.doi.org/10.1007/s11548-023-02939-6
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