Weakly Supervised Recognition of Surgical Gestures
This work addresses the need for efficient surgical skill assessment and automation by reducing reliance on manual annotation, though it is incremental as it builds on existing unsupervised methods with weak supervision.
The paper tackled the problem of automatic segmentation of surgical robot trajectories into gestures without extensive manual labeling by using at least one expert demonstration with ground truth to initialize a GMM-based algorithm, resulting in significant performance improvements over standard initialization methods.
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.