Weakly Supervised Recognition of Surgical Gestures
Weakly Supervised Recognition of Surgical Gestures
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DOI：
10.1109/icra.2019.8793696
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Beatrice van Amsterdam;H. Nakawala;E. Momi;D. Stoyanov
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文献类型：
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作者：
Beatrice van Amsterdam;H. Nakawala;E. Momi;D. Stoyanov
从手术机器人记录的运动轨迹包含了手术姿势的信息，并可能编码关于外科医生技能水平的线索。将这些轨迹自动分割成有意义的动作单元可以帮助开发新的手术技能评估指标，并简化手术自动化。最先进的动作识别方法依赖于大型数据集的手动标记，这是耗时且容易出错的。为了克服这些限制，开发了无监督的方法。然而，它们通常依赖于繁琐的参数调整，并且表现不如监督方法好，特别是在具有高度可变性的数据（如手术轨迹）上。因此，弱监督的潜力可以改善无监督学习，同时避免人工标注大型数据集。在本文中，我们使用至少一个专家演示及其基础真值注释来为基于gmm的手势识别算法生成适当的初始化。我们在实际的手术演示中表明，后者明显优于标准的任务不可知初始化方法。我们还演示了如何通过重新定义动作和优化输入来进一步提高识别精度。
Kinematic trajectories recorded from surgical robots contain information about surgical gestures and potentially encode cues about surgeon’s skill levels. Automatic segmentation of these trajectories into meaningful action units could help to develop new metrics for surgical skill assessment as well as to simplify surgical automation. State-of-the-art methods for action recognition relied on manual labelling of large datasets, which is time consuming and error prone. Unsupervised methods have been developed to overcome these limitations. However, they often rely on tedious parameter tuning and perform less well than supervised approaches, especially on data with high variability such as surgical trajectories. Hence, the potential of weak supervision could be to improve unsupervised learning while avoiding manual annotation of large datasets. In this paper, we used at a minimum one expert demonstration and its ground truth annotations to generate an appropriate initialization for a GMM-based algorithm for gesture recognition. We showed on real surgical demonstrations that the latter significantly outperforms standard task-agnostic initialization methods. We also demonstrated how to improve the recognition accuracy further by redefining the actions and optimising the inputs.