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.
- Random Forest
- Dynamic Time Warping
- Phase Detection
- Medical Image Analysis
- Splitting Rule
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Stauder, R. et al. (2014). Random Forests for Phase Detection in Surgical Workflow Analysis. In: Stoyanov, D., Collins, D.L., Sakuma, I., Abolmaesumi, P., Jannin, P. (eds) Information Processing in Computer-Assisted Interventions. IPCAI 2014. Lecture Notes in Computer Science, vol 8498. Springer, Cham. https://doi.org/10.1007/978-3-319-07521-1_16
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