3 March 2017 Real-time phase recognition in novel needle-based intervention: a multi-operator feasibility study
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Purpose. One of the goals of new navigation systems in the operating room and in outpatient clinics is to support the surgeon's decision making while minimizing the additional load on surrounding health personnel. To do so, the system needs to rely on context-awareness providing the surgeon with the most relevant visualization at all times. Such a system could also provide support for surgical training. The objective of this work is to assess the feasibility of an automatic surgical phase recognition using tracking data from a novel instrument for injections and biopsies. Methods. An injection into the sphenopalatine ganglion planned with MRI and CT images is carried out using optical tracking of the instrument. In the context of a feasibility study, the intervention was performed by 5 operators, each 5 times, on a specially designed phantom. The coordinate information is processed into 7 features characterizing the intervention. Three classifiers, Hidden Markov Model (HMM), a Support Vector Machine (SVM), and a combination of these (SVM+HMM) are trained on manually annotated data and cross-validated for intra- and inter-operator variability. Standard test metrics are used to compare the performance of each classifier. Results. HMM alone and SVM alone are comparable classifiers, but feeding the output of the SVM into an HMM results in significantly better classifications: accuracy of 97.8%, sensitivity of 93.1% and specificity of 98.4%. Conclusion. The use of trajectory information can provide a robust real-time phase recognition of surgical phases for needle-based interventions.
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Sébastien Muller, Sébastien Muller, Fabien Despinoy, Fabien Despinoy, Daniel Bratbak, Daniel Bratbak, Erling Tronvik, Erling Tronvik, Pierre Jannin, Pierre Jannin, } "Real-time phase recognition in novel needle-based intervention: a multi-operator feasibility study", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101350E (3 March 2017); doi: 10.1117/12.2253619; https://doi.org/10.1117/12.2253619

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