Olga Dergachyova1,2, David Bouget3,4, Arnaud Huaulmé3,4,5, Xavier Morandi3,4,6, Pierre Jannin3,4. 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.
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.
Authors: Matthew Stephen Holden; Tamas Ungi; Derek Sargent; Robert C McGraw; Elvis C S Chen; Sugantha Ganapathy; Terry M Peters; Gabor Fichtinger Journal: IEEE Trans Biomed Eng Date: 2014-06 Impact factor: 4.538
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