GazeScope: A Framework of Gaze Attention-Based Automatic Field-of-View Adjustment for Laparoscopic Robots

The procedure of laparoscopic minimally invasive surgery (MIS) heavily relies on the effective and efficient adjustment of the laparoscopic field-of-view (FoV). However, most existing robot-assisted laparoscopic FoV adjustment methods either require additional surgeon interactions or neglect surgeon's intentions. This paper therefore proposes GazeScope, a novel framework for automatic laparoscopic FoV adjustment, which considers gaze attention, the positions of surgical tools in the image, and the eye-hand consistency. A gaze attention-based FoV unlocking strategy is proposed to identify and eliminate unnecessary FoV adjustments, thereby improving the stability of the surgical view. Compared to traditional image-based visual servoing (IBVS) methods, GazeScope offers FoV adjustment that better aligns with manual adjustments by professionals. Meanwhile, GazeScope reduces unnecessary FoV adjustments by at least 66% and adjusting time by 50% in representative test cases, demonstrating its ability to provide a more stable operational view.