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Visual modelling and evaluation of surgical skill

机译：视觉建模和手术技能评估

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摘要

For many surgical procedures, computer-based training has become an increasingly attractive alternative to traditional training methods. One of the key problems in computer-based surgical training is automatic skill evaluation, which in turn requires skill modelling. To model and evaluate human skills it is necessary to provide the means by which the skills can be measured and interpreted by computers. This paper proposes a new approach for observing continuous, long sequence of hand movements in surgical operation, and then modelling and evaluating the skill demonstrated in the observation. This involves a video-based technique for tracking the hand during a surgical exercise. Because of the non-contact nature of the tracking technique, there is minimal interference with the skill execution, unlike other methods that employ instrumented gloves. To increase the robustness of hand tracking, a Kalman filter (see Appendix A for the system and measurement model) is employed together with a set of coloured markers on the surgical glove. For modelling the surgical skill, a stochastic approach is proposed that uses Hidden Markov Models (HMMs). Using this technique, person-independent models can be developed through human demonstration of particular surgical skills. To automatically evaluate a person's skill, an objective evaluation criterion is proposed that is based on the log probability of an observation sequence for the given HMM. The probability measures the stochastic similarity between the performance of the observation sequence and the performance represented by the model. This paper also describes an implemented prototype system and experiments that establish the feasibility of the proposed approach for surgical skill modelling and evaluation.
机译：对于许多外科手术而言，基于计算机的培训已成为传统培训方法的一种越来越有吸引力的替代方法。基于计算机的外科培训中的关键问题之一是自动技能评估，这又需要技能建模。为了对人类技能进行建模和评估，必须提供一种可以通过计算机测量和解释技能的方法。本文提出了一种新的方法，用于观察手术操作中连续，长序列的手部运动，然后对观察中展示的技能进行建模和评估。这涉及一种基于视频的技术，用于在外科手术过程中跟踪手。由于跟踪技术的非接触性质，与其他采用仪器手套的方法不同，对技能执行的干扰最小。为了提高手部跟踪的鲁棒性，将卡尔曼滤波器（有关系统和测量模型，请参见附录A）与手术手套上的一组彩色标记一起使用。为了对外科手术技能进行建模，提出了一种使用隐马尔可夫模型（HMM）的随机方法。使用这种技术，可以通过人类对特定手术技能的演示来开发独立于人的模型。为了自动评估一个人的技能，提出了一个客观评估标准，该标准基于给定HMM的观察序列的对数概率。概率测量观察序列的性能与模型表示的性能之间的随机相似性。本文还介绍了一个已实现的原型系统和实验，这些实验和实验确定了所提出的方法用于手术技能建模和评估的可行性。

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