  Automatic surgical gesture recognition is fundamental for improving
intelligence in robot-assisted surgery, such as conducting complicated tasks of
surgery surveillance and skill evaluation. However, current methods treat each
frame individually and produce the outcomes without effective consideration on
future information. In this paper, we propose a framework based on
reinforcement learning and tree search for joint surgical gesture segmentation
and classification. An agent is trained to segment and classify the surgical
video in a human-like manner whose direct decisions are re-considered by tree
search appropriately. Our proposed tree search algorithm unites the outputs
from two designed neural networks, i.e., policy and value network. With the
integration of complementary information from distinct models, our framework is
able to achieve the better performance than baseline methods using either of
the neural networks. For an overall evaluation, our developed approach
consistently outperforms the existing methods on the suturing task of JIGSAWS
dataset in terms of accuracy, edit score and F1 score. Our study highlights the
utilization of tree search to refine actions in reinforcement learning
framework for surgical robotic applications.
