Modeling Surgical Procedures for Multimodal Image-Guided Neurosurgery
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
Real-time identification of operating room state from video
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Eye-gaze driven surgical workflow segmentation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
A boosted segmentation method for surgical workflow analysis
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Propagation networks for recognition of partially ordered sequential action
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
HMM assessment of quality of movement trajectory in laparoscopic surgery
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Automatic detection and segmentation of robot-assisted surgical motions
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Acquisition of process descriptions from surgical interventions
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Modeling and Online Recognition of Surgical Phases Using Hidden Markov Models
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Minimally invasive surgery maneuver recognition based on surgeon model
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Modeling and segmentation of surgical workflow from laparoscopic video
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Surgical phases detection from microscope videos by combining SVM and HMM
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Discovery of high-level tasks in the operating room
Journal of Biomedical Informatics
An application-dependent framework for the recognition of high-level surgical tasks in the OR
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Classification of surgical processes using dynamic time warping
Journal of Biomedical Informatics
Activity recognition for emergency care using RFID
Proceedings of the 6th International Conference on Body Area Networks
Intervention time prediction from surgical low-level tasks
Journal of Biomedical Informatics
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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.