A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cutting

By Thanh Thi Nguyen, Ngoc Duy Nguyen, Fernando Bello, Saeid Nahavandi. 2019

42 citations recorded by the source corpus.

Surgeons normally need surgical scissors and tissue grippers to cut through a deformable surgical tissue. The cutting accuracy depends on the skills to manipulate these two tools. Such skills are part of basic surgical skills training as in the Fundamentals of Laparoscopic Surgery. The gripper is used to pinch a point on the surgical sheet and pull the tissue to a certain direction to maintain the tension while the scissors cut through a trajectory. As the surgical materials are deformable, it requires a comprehensive tensioning policy to yield appropriate tensioning direction at each step of the cutting process. Automating a tensioning policy for a given cutting trajectory will support not only the human surgeons but also the surgical robots to improve the cutting accuracy and reliability. This paper presents a multiple pinch point approach to modelling an autonomous tensioning planner based on a deep reinforcement learning algorithm. Experiments on a simulator show that the proposed method is superior to existing methods in terms of both performance and robustness.

Subfields: Biomedical Engineering, Computational Mechanics, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati

In Computational Mechanics

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

In Control and Systems Engineering

1. Shamim Nemati
2. Vijay Janappa Reddi
3. Chao Yu
4. Thanh Thi Nguyen
5. Jiming Liu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “Thananjeyan et al. [220] and Nguyen et al. [168] applied the DRL algorithm, TRPO, in learning tensioning policies effectively for surgical gauze cutting.”
• Deep Reinforcement Learning for Cyber Security (2019, background): “Atari [28], [29], game of Go [30], [31], realtime strategy game StarCraft II [32]–[35], 3D multi-player game Quake III Arena Capture the Flag [36], and teamwork game Dota 2 [37] to real-world applications such as robotics [38], autonomous vehicles [39], autonomous surgery [40],”