Deakin University
Browse

File(s) under permanent embargo

A new tensioning method using deep reinforcement learning for surgical pattern cutting

conference contribution
posted on 2019-01-01, 00:00 authored by Thanh Thi NguyenThanh Thi Nguyen, Ngoc Duy Nguyen, F Bello, Saeid Nahavandi
© 2019 IEEE. 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.

History

Event

Industrial Technology. International Conference (2019 : Melbourne, Victoria)

Volume

2019-February

Pagination

1339 - 1344

Publisher

IEEE

Location

Melbourne, Victoria

Place of publication

Piscataway, N.J.

Start date

2019-02-13

End date

2019-02-15

ISBN-13

9781538663769

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2019, IEEE

Title of proceedings

ICIT 2019 : IEEE International Conference on Industrial Technology

Usage metrics

    Research Publications

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC