title: Automatic data-driven real-time segmentation and recognition of surgical workflow.
abstract: OBJECTIVE
  With the intention of extending the perception and action of surgical staff inside the operating room, the medical community has expressed a growing interest towards context-aware systems. Requiring an accurate identification of the surgical workflow, such systems make use of data from a diverse set of available sensors. In this paper, we propose a fully data-driven and real-time method for segmentation and recognition of surgical phases using a combination of video data and instrument usage signals, exploiting no prior knowledge. We also introduce new validation metrics for assessment of workflow detection.
  
  
  METHODS
  The segmentation and recognition are based on a four-stage process. Firstly, during the learning time, a Surgical Process Model is automatically constructed from data annotations to guide the following process. Secondly, data samples are described using a combination of low-level visual cues and instrument information. Then, in the third stage, these descriptions are employed to train a set of AdaBoost classifiers capable of distinguishing one surgical phase from others. Finally, AdaBoost responses are used as input to a Hidden semi-Markov Model in order to obtain a final decision.
  
  
  RESULTS
  On the MICCAI EndoVis challenge laparoscopic dataset we achieved a precision and a recall of 91 % in classification of 7 phases.
  
  
  CONCLUSIONS
  Compared to the analysis based on one data type only, a combination of visual features and instrument signals allows better segmentation, reduction of the detection delay and discovery of the correct phase order.
authors.affiliation: INSERM, U1099, Rennes, 35000, France. olga.dergachyova@univ-rennes1.fr.|Université de Rennes 1, LTSI, Rennes, 35000, France. olga.dergachyova@univ-rennes1.fr.
authors.forename: Olga
authors.identifier: http://orcid.org/0000-0001-7023-9162
authors.initials: O
authors.lastname: Dergachyova
authors.affiliation: INSERM, U1099, Rennes, 35000, France.|Université de Rennes 1, LTSI, Rennes, 35000, France.
authors.forename: David
authors.initials: D
authors.lastname: Bouget
authors.affiliation: INSERM, U1099, Rennes, 35000, France.|Université de Rennes 1, LTSI, Rennes, 35000, France.|Université Joseph Fourier, TIMC-IMAG UMR 5525, Grenoble, 38041, France.
authors.forename: Arnaud
authors.initials: A
authors.lastname: Huaulmé
authors.affiliation: INSERM, U1099, Rennes, 35000, France.|Université de Rennes 1, LTSI, Rennes, 35000, France.|CHU Rennes, Département de Neurochirurgie, Rennes, 35000, France.
authors.forename: Xavier
authors.initials: X
authors.lastname: Morandi
authors.affiliation: INSERM, U1099, Rennes, 35000, France.|Université de Rennes 1, LTSI, Rennes, 35000, France.
authors.forename: Pierre
authors.initials: P
authors.lastname: Jannin
mesh_terms: D000465:Algorithms; D003625:Data Collection; D006801:Humans; D010535:Laparoscopy; D008953:Models, Anatomic; D009873:Operating Rooms; D025321:Surgery, Computer-Assisted / Q000379:methods*; D013647:Task Performance and Analysis; D014741:Video Recording; D057188:Workflow
keywords: AdaBoost; Computer-assisted surgery; Hidden semi-Markov Model; Surgical Process Modelling; Surgical workflow