MEDiC: Autonomous Surgical Robotic Assistance to Maximizing Exposure for Dissection and Cautery

TL;DR

This paper proposes MEDiC, a framework for autonomous surgical robotic assistance to Maximizing Exposure for Dissection and Cautery, and integrates a differentiable physics model with perceptual feedback to achieve its two key objectives.

Abstract

Surgical automation has the capability to improve the consistency of patient outcomes and broaden access to advanced surgical care in underprivileged communities. Shared autonomy, where the robot automates routine subtasks while the surgeon retains partial teleoperative control, offers great potential to make an impact. In this paper we focus on one important skill within surgical shared autonomy: Automating robotic assistance to maximize visual exposure and apply tissue tension for dissection and cautery. Ensuring consistent exposure to visualize the surgical site is crucial for both efficiency and patient safety. However, achieving this is highly challenging due to the complexities of manipulating deformable volumetric tissues that are prevalent in surgery.To address these challenges we propose \methodname, a framework for autonomous surgical robotic assistance to \methodfullname. We integrate a differentiable physics model with perceptual feedback to achieve our two key objectives: 1) Maximizing tissue exposure and applying tension for a specified dissection site through visual-servoing conrol and 2) Selecting optimal control positions for a dissection target based on deformable Jacobian analysis. We quantitatively assess our method through repeated real robot experiments on a tissue phantom, and showcase its capabilities through dissection experiments using shared autonomy on real animal tissue.

Figures (5)

• Figure 1: Autonomous assistance for tissue dissection in robotic surgery. In response to a assistance request from a human operator, a robotic gripper improves visibility of the dissection site by pushing and stretching the tissue. The gripper's action is informed by a visual servoing controller. The human operator dissects the tissue after the dissection site is exposed.
• Figure 2: Visualization of the geometrical concepts to define our objectives. The top left figure shows how a single feature pair, $v_{r,k}, w_{r,k}$, are found given a dissection line $D$. The top right figure illustrate the idea of selecting multiple rings. In the bottom from left to right, they illustrate wedge expansion, shear regulation, and stretch enforcement objectives.
• Figure 3: Illustration of our tissue state estimation method. Left: Determining tissue visibility by ray-casting and checking the ray-triangle intersections. The circled blue region indicates the visible region of the mesh. Right: Registering the volumetric mesh to the observed surface point cloud.
• Figure 4: A comparison between using our method with the proposed APS, and with a manually selected sub-optimal assistance position. The heatmap visualizes the computed $\mathcal{M}$ scores that APS uses to determine the optimal position. Our method with APS achieves significantly more expansion of the dissection target than the suboptimal manual selection.
• Figure 5: This figure illustrates the execution of five consecutive dissection assistance requests followed by manual dissection on real animal tissue. For each request, our method first locates the best assistance position and uses visual-servoing control to achieve the assistance objectives. The human operator subsequently dissects the tissue with an electric cautery tool. The cyan regions near the dissection goals are expanded and tensioned to the larger blue region after application of our method. Between every request, we re-initalize the simulation to account for dissection effects.