Automatic data-driven real-time segmentation and recognition of surgical workflow

Int J Comput Assist Radiol Surg. 2016 Jun;11(6):1081-9. doi: 10.1007/s11548-016-1371-x. Epub 2016 Mar 19.


Purpose: 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.

Conclusion: 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.

Keywords: AdaBoost; Computer-assisted surgery; Hidden semi-Markov Model; Surgical Process Modelling; Surgical workflow.

MeSH terms

  • Algorithms*
  • Data Collection
  • Humans
  • Laparoscopy*
  • Models, Anatomic
  • Operating Rooms
  • Surgery, Computer-Assisted / methods*
  • Task Performance and Analysis*
  • Video Recording
  • Workflow*