Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Computers
Fusion of feature selection methods for pairwise scoring SVM
Neurocomputing
ISBMS '08 Proceedings of the 4th international symposium on Biomedical Simulation
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
On-line recognition of surgical activity for monitoring in the operating room
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Eye-gaze driven surgical workflow segmentation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Automatic phases recognition in pituitary surgeries by microscope images classification
IPCAI'10 Proceedings of the First international conference on Information processing in computer-assisted interventions
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
Intervention time prediction from surgical low-level tasks
Journal of Biomedical Informatics
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In order to better understand and describe surgical procedures by surgical process models, the field of workflow segmentation has recently emerged. It aims to recognize high-level surgical tasks in the Operating Room, with the help of sensors or human-based systems. Our approach focused on the automatic recognition of surgical phases by microscope images analysis. We used a hybrid method that combined Support Vector Machine and discrete Hidden Markov Model. We first performed features extraction and selection on surgical microscope frames to create an image database. SVMs were trained to extract surgical scene information, and then outputs were used as observations for training a discrete HMM. Our framework was tested on pituitary surgery, where six phases were identified by neurosurgeons. Cross-validation studies permitted to find a percentage of detected phases of 93% that will allow the use of the system in clinical applications such as post-operative videos indexation.