Surgical data processing for smart intraoperative assistance systems
Stauder R, Ostler D, Vogel T, Wilhelm D, Koller S, Kranzfelder M, Navab N (2020)
Publication Type: Journal article, Review article
Publication year: 2020
Journal
Book Volume: 2
Pages Range: 145-152
Journal Issue: 3
Abstract
Different components of the newly defined field of surgical data science have been under research at our groups for more than a decade now. In this paper, we describe our sensor-driven approaches to workflow recognition without the need for explicit models, and our current aim is to apply this knowledge to enable context-aware surgical assistance systems, such as a unified surgical display and robotic assistance systems. The methods we evaluated over time include dynamic time warping, hidden Markov models, random forests, and recently deep neural networks, specifically convolutional neural networks.
Involved external institutions
Germany (DE)How to cite
APA:
Stauder, R., Ostler, D., Vogel, T., Wilhelm, D., Koller, S., Kranzfelder, M., & Navab, N. (2020). Surgical data processing for smart intraoperative assistance systems. Innovative Surgical Science, 2(3), 145-152. https://doi.org/10.1515/iss-2017-0035
MLA:
Stauder, Ralf, et al. "Surgical data processing for smart intraoperative assistance systems." Innovative Surgical Science 2.3 (2020): 145-152.
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