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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.

Author information

1
INSERM, U1099, Rennes, 35000, France. olga.dergachyova@univ-rennes1.fr.
2
Université de Rennes 1, LTSI, Rennes, 35000, France. olga.dergachyova@univ-rennes1.fr.
3
INSERM, U1099, Rennes, 35000, France.
4
Université de Rennes 1, LTSI, Rennes, 35000, France.
5
Université Joseph Fourier, TIMC-IMAG UMR 5525, Grenoble, 38041, France.
6
CHU Rennes, Département de Neurochirurgie, Rennes, 35000, France.

Abstract

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

PMID:
26995598
DOI:
10.1007/s11548-016-1371-x
[Indexed for MEDLINE]

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