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

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

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INSERM, U1099, Rennes, 35000, France.
Université de Rennes 1, LTSI, Rennes, 35000, France.
INSERM, U1099, Rennes, 35000, France.
Université de Rennes 1, LTSI, Rennes, 35000, France.
Université Joseph Fourier, TIMC-IMAG UMR 5525, Grenoble, 38041, France.
CHU Rennes, Département de Neurochirurgie, Rennes, 35000, France.



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.


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.


On the MICCAI EndoVis challenge laparoscopic dataset we achieved a precision and a recall of 91 % in classification of 7 phases.


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.


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

[Indexed for MEDLINE]

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