Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation

In Minimally Invasive Surgery (MIS), tissue scanning with imaging probes is required for subsurface visualisation to characterise the state of the tissue. However, scanning of large tissue surfaces in the presence of deformation is a challenging task for the surgeon. Recently, robot-assisted local tissue scanning has been investigated for motion stabilisation of imaging probes to facilitate the capturing of good quality images and reduce the surgeon's cognitive load. Nonetheless, these approaches require the tissue surface to be static or deform with periodic motion. To eliminate these assumptions, we propose a visual servoing framework for autonomous tissue scanning, able to deal with free-form tissue deformation. The 3D structure of the surgical scene is recovered and a feature-based method is proposed to estimate the motion of the tissue in real-time. A desired scanning trajectory is manually defined on a reference frame and continuously updated using projective geometry to follow the tissue motion and control the movement of the robotic arm. The advantage of the proposed method is that it does not require the learning of the tissue motion prior to scanning and can deal with free-form deformation. We deployed this framework on the da Vinci surgical robot using the da Vinci Research Kit (dVRK) for Ultrasound tissue scanning. Since the framework does not rely on information from the Ultrasound data, it can be easily extended to other probe-based imaging modalities.Comment: 7 pages, 5 figures, ICRA 202

Complementary Situational Awareness for an Intelligent Telerobotic Surgical Assistant System

Robotic surgical systems have contributed greatly to the advancement of Minimally Invasive Surgeries (MIS). More specifically, telesurgical robots have provided enhanced dexterity to surgeons performing MIS procedures. However, current robotic teleoperated systems have only limited situational awareness of the patient anatomy and surgical environment that would typically be available to a surgeon in an open surgery. Although the endoscopic view enhances the visualization of the anatomy, perceptual understanding of the environment and anatomy is still lacking due to the absence of sensory feedback. In this work, these limitations are addressed by developing a computational framework to provide Complementary Situational Awareness (CSA) in a surgical assistant. This framework aims at improving the human-robot relationship by providing elaborate guidance and sensory feedback capabilities for the surgeon in complex MIS procedures. Unlike traditional teleoperation, this framework enables the user to telemanipulate the situational model in a virtual environment and uses that information to command the slave robot with appropriate admittance gains and environmental constraints. Simultaneously, the situational model is updated based on interaction of the slave robot with the task space environment. However, developing such a system to provide real-time situational awareness requires that many technical challenges be met. To estimate intraoperative organ information continuous palpation primitives are required. Intraoperative surface information needs to be estimated in real-time while the organ is being palpated/scanned. The model of the task environment needs to be updated in near real-time using the estimated organ geometry so that the force-feedback applied on the surgeon's hand would correspond to the actual location of the model. This work presents a real-time framework that meets these requirements/challenges to provide situational awareness of the environment in the task space. Further, visual feedback is also provided for the surgeon/developer to view the near video frame rate updates of the task model. All these functions are executed in parallel and need to have a synchronized data exchange. The system is very portable and can be incorporated to any existing telerobotic platforms with minimal overhead

A framework for sensorless and autonomous probe-tissue contact management in robotic endomicroscopic scanning

Advances in optical imaging, and probe-based Confocal Laser Endomicroscopy (pCLE) in particular, offer real-time cellular level information for in-vivo tissue characterization. However for large area coverage, the limited field-of-view necessitates the use of a technique known as mosaicking to generate usable information from the incoming image stream. Mosaicking also needs a continuous stream of good quality images, but this is challenging as the probe needs to be maintained within an optimal working range and the contact force controlled to minimize tissue deformation. Robotic manipulation presents a potential solution to these challenges, but the lack of haptic feedback in current surgical robot systems hinders the technology's clinical adoption. This paper proposes a sensorless alternative based on processing the incoming image stream and deriving a quantitative measure representative of the image quality. This measure is then used by a controller, designed using model-free reinforcement learning techniques, to maintain optimal contact autonomously. The developed controller has shown near real-time performance in overcoming typical loss-of-contact and excess-deformation scenarios experienced during endomicroscopy scanning procedures