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Article Dans Une Revue International Journal of Computer Assisted Radiology and Surgery Année : 2016

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

Résumé

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.
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Dates et versions

hal-01299344 , version 1 (11-04-2016)

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Olga Dergachyova, David Bouget, Arnaud Huaulmé, Xavier Morandi, Pierre Jannin. Automatic data-driven real-time segmentation and recognition of surgical workflow. International Journal of Computer Assisted Radiology and Surgery, 2016, 11 (6), pp.1081-1089. ⟨10.1007/s11548-016-1371-x⟩. ⟨hal-01299344⟩
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