SurgeMOD: Translating image-space tissue motions into vision-based surgical forces

TL;DR

SurgeMOD introduces a novel vision-based force estimation method in minimally invasive surgery by analyzing organ motion in the frequency domain, enabling reliable force inference from video data without direct force sensors.

Contribution

This work is the first to leverage frequency domain motion analysis from surgical videos for force estimation, integrating a mechanical model with dynamic constraints.

Findings

Accurately estimates contact forces in phantom and ex-vivo experiments

Matches real force sensor readings with high reliability

Synthesizes coherent force textures from surgical videos

Abstract

We present a new approach for vision-based force estimation in Minimally Invasive Robotic Surgery based on frequency domain basis of motion of organs derived directly from video. Using internal movements generated by natural processes like breathing or the cardiac cycle, we infer the image-space basis of the motion on the frequency domain. As we are working with this representation, we discretize the problem to a limited amount of low-frequencies to build an image-space mechanical model of the environment. We use this pre-built model to define our force estimation problem as a dynamic constraint problem. We demonstrate that this method can estimate point contact forces reliably for silicone phantom and ex-vivo experiments, matching real readings from a force sensor. In addition, we perform qualitative experiments in which we synthesize coherent force textures from surgical videos over a certain region of interest selected by the user. Our method demonstrates good results for both quantitative and qualitative analysis, providing a good starting point for a purely vision-based method for surgical force estimation.