SurgeMOD: Translating image-space tissue motions into vision-based surgical forces
This addresses the challenge of force estimation in surgery without physical sensors, but it is incremental as it builds on existing vision-based methods with a new frequency domain approach.
The paper tackles the problem of vision-based force estimation in Minimally Invasive Robotic Surgery by translating image-space tissue motions into forces using a frequency domain basis derived from video, and demonstrates reliable point contact force estimation matching sensor readings in phantom and ex-vivo experiments.
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.