Open-Source Multi-Viewpoint Surgical Telerobotics

As robots for minimally invasive surgery (MIS) gradually become more accessible and modular, we believe there is a great opportunity to rethink and expand the visualization and control paradigms that have characterized surgical teleoperation since its inception. We conjecture that introducing one or more additional adjustable viewpoints in the abdominal cavity would not only unlock novel visualization and collaboration strategies for surgeons but also substantially boost the robustness of machine perception toward shared autonomy. Immediate advantages include controlling a second viewpoint and teleoperating surgical tools from a different perspective, which would allow collaborating surgeons to adjust their views independently and still maneuver their robotic instruments intuitively. Furthermore, we believe that capturing synchronized multi-view 3D measurements of the patient's anatomy would unlock advanced scene representations. Accurate real-time intraoperative 3D perception will allow algorithmic assistants to directly control one or more robotic instruments and/or robotic cameras. Toward these goals, we are building a synchronized multi-viewpoint, multi-sensor robotic surgery system by integrating high-performance vision components and upgrading the da Vinci Research Kit control logic. This short paper reports a functional summary of our setup and elaborates on its potential impacts in research and future clinical practice. By fully open-sourcing our system, we will enable the research community to reproduce our setup, improve it, and develop powerful algorithms, effectively boosting clinical translation of cutting-edge research.

MultiViPerFrOG: A Globally Optimized Multi-Viewpoint Perception Framework for Camera Motion and Tissue Deformation

Reconstructing the 3D shape of a deformable environment from the information captured by a moving depth camera is highly relevant to surgery. The underlying challenge is the fact that simultaneously estimating camera motion and tissue deformation in a fully deformable scene is an ill-posed problem, especially from a single arbitrarily moving viewpoint. Current solutions are often organ-specific and lack the robustness required to handle large deformations. Here we propose a multi-viewpoint global optimization framework that can flexibly integrate the output of low-level perception modules (data association, depth, and relative scene flow) with kinematic and scene-modeling priors to jointly estimate multiple camera motions and absolute scene flow. We use simulated noisy data to show three practical examples that successfully constrain the convergence to a unique solution. Overall, our method shows robustness to combined noisy input measures and can process hundreds of points in a few milliseconds. MultiViPerFrOG builds a generalized learning-free scaffolding for spatio-temporal encoding that can unlock advanced surgical scene representations and will facilitate the development of the computer-assisted-surgery technologies of the future.

Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue

New sensing technologies and more advanced processing algorithms are transforming computer-integrated surgery. While researchers are actively investigating depth sensing and 3D reconstruction for vision-based surgical assistance, it remains difficult to achieve real-time, accurate, and robust 3D representations of the abdominal cavity for minimally invasive surgery. Thus, this work uses quantitative testing on fresh ex-vivo porcine tissue to thoroughly characterize the quality with which a 3D laser-based time-of-flight sensor (lidar) can perform anatomical surface reconstruction. Ground-truth surface shapes are captured with a commercial laser scanner, and the resulting signed error fields are analyzed using rigorous statistical tools. When compared to modern learning-based stereo matching from endoscopic images, time-of-flight sensing demonstrates higher precision, lower processing delay, higher frame rate, and superior robustness against sensor distance and poor illumination. Furthermore, we report on the potential negative effect of near-infrared light penetration on the accuracy of lidar measurements across different tissue samples, identifying a significant measured depth offset for muscle in contrast to fat and liver. Our findings highlight the potential of lidar for intraoperative 3D perception and point toward new methods that combine complementary time-of-flight and spectral imaging.