On-line Recognition of Surgical Activity for Monitoring in the Operating Room

Surgery rooms are complex environments where many interactions take place between staff members and the electronic and mechanical systems. In spite of their inherent complexity, surgeries of the same kind bear numerous similarities and are usually performed with similar workftows. This gives the possibility to design support systems in the Operating Room (OR), whose applicability range from easy tasks such as the activation of OR lights and calling the next patient, to more complex ones such as context-sensitive user interfaces or automatic reporting. An essential feature when designing such systems, is the ability for on-line recognition of what is happening inside the OR, based on recorded signals. In this paper, we present an approach using signals from the OR and Hidden Markov Models to recognize on-line the surgical steps performed by the surgeon during a laparoscopic surgery. We also explain how the system can be deployed in the OR. Experiments are presented using 11 real surgeries performed by different surgeons in several ORs, recorded at our partner hospital. We believe that similar systems will quickly develop in the near future in order to efficiently support surgeons, trainees and the medical staff in general, as well as to improve administrative tasks like scheduling within hospitals.

[1]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[2]  Heinrich Niemann,et al.  Color cluster rotation , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[3]  Bernard Gibaud,et al.  Modeling Surgical Procedures for Multimodal Image-Guided Neurosurgery , 2001, MICCAI.

[4]  Guang-Zhong Yang,et al.  Episode Classification for the Analysis of Tissue/Instrument Interaction with Multiple Visual Cues , 2003, MICCAI.

[5]  C. Herfarth ‘Lean’ surgery through changes in surgical work flow , 2003, The British journal of surgery.

[6]  Yan Huang,et al.  Propagation networks for recognition of partially ordered sequential action , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[7]  Eric Horvitz,et al.  Layered representations for learning and inferring office activity from multiple sensory channels , 2004, Comput. Vis. Image Underst..

[8]  David C. Minnen,et al.  Propagation networks for recognition of partially ordered sequential action , 2004, CVPR 2004.

[9]  Gregory D. Hager,et al.  Automatic Detection and Segmentation of Robot-Assisted Surgical Motions , 2005, MICCAI.

[10]  Kevin Cleary,et al.  OR 2020: the operating room of the future. , 2004, Journal of laparoendoscopic & advanced surgical techniques. Part A.

[11]  Guang-Zhong Yang,et al.  HMM Assessment of Quality of Movement Trajectory in Laparoscopic Surgery , 2006, MICCAI.

[12]  Gero Strauß,et al.  Acquisition of Process Descriptions from Surgical Interventions , 2006, DEXA.

[13]  Blake Hannaford,et al.  Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model , 2006, IEEE Transactions on Biomedical Engineering.

[14]  Guang-Zhong Yang,et al.  Eye-Gaze Driven Surgical Workflow Segmentation , 2007, MICCAI.

[15]  Nassir Navab,et al.  A Boosted Segmentation Method for Surgical Workflow Analysis , 2007, MICCAI.

[16]  Nassir Navab,et al.  Recovery of Surgical Workflow: a Model-based Approach , 2007 .

[17]  Peter Fu-Ming Hu,et al.  Real-Time Identification of Operating Room State from Video , 2007, AAAI.