Intraoperative fusion of models and data for robust distance sensing

Instrument-integrated optical sensors are gaining popularity in microsurgery due to their ability to provide accurate measurements of instrument-to-tissue distances, enabling precise instrument control. However, obstructions in the optical path can result in measurement errors. In this work, we propose a method to improve robustness of distance information from sensorized microsurgical instruments.

Our pipeline integrates a rapid search algorithm to identify relevant neighboring data points, as well as geometric and non-geometric techniques to accurately model the local tissue structure. Additionally, we implement a fusion of measurement and model to identify and overcome disturbances, e.g., obstructions from surgical instruments or semantic segmentation errors.

Our simulation examines the effect of different modeling parameters and techniques on distance prediction, yielding a mean absolute error of less than 0.02 mm when using the local spline fit. Experiments in ex vivo human eyes show that our pipeline achieves up to 89 % error reduction when compared to sensor only.

Our method improves the reliability of instrument-integrated optical sensors. This work could enable distance-based instrument control in challenging conditions, thereby enhancing surgical precision in delicate ophthalmic procedures. Our approach can be generalized to any surgery with sensorized instruments and beyond.