Estimation of Tissue Deformation and Interactive Force in Robotic Surgery through Vision-based Learning
Journal:
arXiv
Published Date:
Apr 29, 2025
Abstract
Goal: A limitation in robotic surgery is the lack of force feedback, due to
challenges in suitable sensing techniques. To enhance the perception of the
surgeons and precise force rendering, estimation of these forces along with
tissue deformation level is presented here. Methods: An experimental test bed
is built for studying the interaction, and the forces are estimated from the
raw data. Since tissue deformation and stiffness are non-linearly related, they
are independently computed for enhanced reliability. A Convolutional Neural
Network (CNN) based vision model is deployed, and both classification and
regression models are developed. Results: The forces applied on the tissue are
estimated, and the tissue is classified based on its deformation. The exact
deformation of the tissue is also computed. Conclusions: The surgeons can
render precise forces and detect tumors using the proposed method. The rarely
discussed efficacy of computing the deformation level is also demonstrated.
