Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation
This work addresses a critical problem for surgeons in minimally invasive surgery by enabling autonomous scanning under unpredictable tissue deformation, though it is incremental as it builds on existing robot-assisted scanning methods.
The paper tackles the challenge of scanning large, deforming tissue surfaces in minimally invasive surgery by proposing a visual servoing framework for autonomous tissue scanning that handles free-form tissue deformation, achieving real-time motion estimation and control on a da Vinci surgical robot without prior learning of tissue motion.
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.