Random forests for phase detection in surgical workflow analysis
Stauder R, Okur A, Peter L, Schneider A, Kranzfelder M, Feussner H, Navab N (2014)
Publication Type: Conference contribution
Publication year: 2014
Journal
Publisher: Springer Verlag
Book Volume: 8498 LNCS
Pages Range: 148-157
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: JPN
ISBN: 9783319075204
DOI: 10.1007/978-3-319-07521-1_16
Abstract
Identifying and recognizing the workflow of surgical interventions is a field of growing interest. Several methods have been developed to identify intra-operative activities, detect common phases in the surgical workflow and combine the gained knowledge into Surgical Process Models. Numerous applications of this knowledge are conceivable, from semi-automatic report generation, teaching and objective surgeon evaluation to context-aware operating rooms and simulation of interventions to optimize the operating room layout. In this work we propose a method to utilize random decision forests to detect surgical workflow phases based on instrument usage data and other, easily obtainable measurements. While decision forests have become a very versatile and popular tool in the field of medical image analysis, this is to the best of our knowledge its first application to surgical workflow analysis. Our method is in principle suitable for online usage and does not rely on an explicit model or a strict temporal relationship between observations. With their structure, random forests are inherently suited for multi-class detection and therefore for detection of workflow phases. Due to the transparent nature of random forests, additional information may also be obtainable in parallel to the phase detection. © 2014 Springer International Publishing Switzerland.
Involved external institutions
Germany (DE)How to cite
APA:
Stauder, R., Okur, A., Peter, L., Schneider, A., Kranzfelder, M., Feussner, H., & Navab, N. (2014). Random forests for phase detection in surgical workflow analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 148-157). JPN: Springer Verlag.
MLA:
Stauder, Ralf, et al. "Random forests for phase detection in surgical workflow analysis." Proceedings of the 5th International Conference on Information Processing in Computer-Assisted Interventions, IPCAI 2014, JPN Springer Verlag, 2014. 148-157.
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