Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

R. DiPietro, R. Stauder, E. Kayis, A. Schneider, M. Kranzfelder, H. Feußner, G. D. Hager, N. Navab
Automated Surgical-Phase Recognition Using Rapidly-Deployable Sensors
MICCAI Workshop on Modeling and Monitoring of Computer Assisted Interventions (M2CAI), Munich, Germany, October 2015 (bib)

Surgical-phase recognition is important to many future applications in clinical care, from building context-aware operating rooms to automatically providing feedback to surgeons in training. In this work, we focus on learning-based phase recognition in laparoscopic gallbladder removals (cholescystectomies). Using data from 15 sensors across 42 surgeries, we 1) compare performance using support vector machines, hidden Markov models, and conditional random fields and 2) demonstrate that it is possible to achieve 74\% accuracy using only 8 rapidly-deployable sensors.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Edit | Attach | Refresh | Diffs | More | Revision r1.13 - 30 Jan 2019 - 15:16 - LeslieCasas

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif