Organization:
Funded by: | Meander Medisch Centrum, Johnson & Johnson |
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chair MIA: Daily supervisors:
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Description:
With the introduction of electronic systems in the operating room, such as patient monitoring, laparoscopic surgery, and robot assistance, more and more data is recorded during surgical procedures. This trend gives data-driven systems, such as machine learning models, the opportunity to gain a more prominent role in the surgical environment.
We explore the development of deep learning based algoritms for scene understanding in operating room videos. For example, by detection of medical staff and the identification of their role, or by recognition of clinical phases in surgical procedures. Automatic scene understanding can form the basis for higher-level algorithms that assist the surgical team, that help to evaluate completed procedures, or that bring insights for surgery planning. We approach the development of new algorithms from the perspective of geometric deep learning, with a focus on end-to-end differentiable methods and graphical models.
In our work, we pay explicit attention to the privacy of the medical staff and the patients. Our goal is to find practical and effective solutions that respect the privacy of the people that enter the observed environment and thereby contributing to the digital trend that makes the operating room safer, more efficient, and more pleasant
Output:
Publications:
NeRF-OR: neural radiance fields for operating room scene reconstruction from sparse-view RGB-D videos (2024)International journal of computer assisted radiology and surgery (E-pub ahead of print/First online). Gerats, B. G. A., Wolterink, J. M. & Broeders, I. A. M. J.https://doi.org/10.1007/s11548-024-03261-5Neural Fields for 3D Tracking of Anatomy and Surgical Instruments in Monocular Laparoscopic Video Clips (2024)[Working paper › Preprint]. ArXiv.org. Gerats, B. G. A., Wolterink, J. M., Mol, S. P. & Broeders, I. A. M. J.https://doi.org/10.48550/arXiv.2403.19265 3D human pose estimation in multi-view operating room videos using differentiable camera projections (2023)Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(4), 1197-1205. Gerats, B. G. A., Wolterink, J. M. & Broeders, I. A. M. J.https://doi.org/10.1080/21681163.2022.2155580Towards an AI-based assessment model of surgical difficulty during early phase laparoscopic cholecystectomy (2023)Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(4), 1299-1306. Abbing, J. R., Voskens, F. J., Gerats, B. G. A., Egging, R. M., Milletari, F. & Broeders, I. A. M. J.https://doi.org/10.1080/21681163.2022.2163296Dynamic Depth-Supervised NeRF for Multi-view RGB-D Operating Room Videos (2023)In Predictive Intelligence in Medicine : 6th International Workshop, PRIME 2023, Held in Conjunction with MICCAI 2023, Proceedings (pp. 218-230) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14277). Springer. Gerats, B. G. A., Wolterink, J. M. & Broeders, I. A. M. J.https://doi.org/10.1007/978-3-031-46005-0_19 Depth-Supervised NeRF for Multi-View RGB-D Operating Room Images (2022)[Working paper › Preprint]. Gerats, B. G. A., Wolterink, J. M. & Broeders, I. A. M. J.Dynamic Depth-Supervised NeRF for Multi-View RGB-D Operating Room Images (2022)[Working paper › Preprint]. ArXiv.org. Gerats, B. G. A., Wolterink, J. M. & Broeders, I. A. M. J.https://doi.org/10.48550/arXiv.2211.124363D human pose estimation in multi-view operating room videos using differentiable camera projections (2022)[Working paper › Preprint]. ArXiv.org. Gerats, B. G. A., Wolterink, J. M. & Broeders, I. A. M. J.https://doi.org/10.48550/arXiv.2210.11826 Pictures:
