Towards optimal camera positioning for navigated surgery applications based on RGBD data

Abstract

Surgical Navigation Systems have significantly enhanced surgical procedures by enabling computer-aided position tracking of medical instruments and the patient's body. Optical Tracking Systems (OTS) have emerged as the leading technology, relying on infrared retroreflective markers for instrument recognition. However, lineof-sight issues, caused by marker occlusion or contamination during surgery, present a significant challenge for OTS. To overcome these limitations, a novel approach is proposed, aiming to eliminate the need for markers and instead employing multiple RGBD-cameras integrated with AI-based techniques and optical-geometrical postprocessing to achieve precise instrument tracking. This paper introduces a simulation utilizing real surgery data to assess occlusions and evaluate visibility during navigated surgery. By capturing depth data with cameras, a realistic occlusion model is constructed, enabling the analysis of visibility in various surgical scenarios. The "task occlusion score" (TOS) is introduced to measure the average occlusion of the target instrument during surgery. Interestingly, the evaluation on a multi-view operating room dataset reveals that optimal camera placement for instrument visibility is above the situs, contrary to conventional setups above the patient's head or feet. The visibility simulation based on real depth data holds potential for enhancing navigated surgery usability. It offers the opportunity to explore visibility in scenarios with moving instruments, different handle shapes, camera models, and positions, without the need for exhaustive real-life tests. This promising technology not only benefits marker-less systems but also provides optimization possibilities for marker-based systems through various marker geometries. Future expansions could extend the simulation to optimize not only visibility but also tracking accuracy with multiple cameras, leveraging existing optimization techniques and metrics from the literature. Ultimately, this approach has the potential to revolutionize navigated surgery, leading to improved surgical outcomes and patient care.