  A significant challenge in image-guided surgery is the accurate measurement
task of relevant structures such as vessel segments, resection margins, or
bowel lengths. While this task is an essential component of many surgeries, it
involves substantial human effort and is prone to inaccuracies. In this paper,
we develop a novel human-AI-based method for laparoscopic measurements
utilizing stereo vision that has been guided by practicing surgeons. Based on a
holistic qualitative requirements analysis, this work proposes a comprehensive
measurement method, which comprises state-of-the-art machine learning
architectures, such as RAFT-Stereo and YOLOv8. The developed method is assessed
in various realistic experimental evaluation environments. Our results outline
the potential of our method achieving high accuracies in distance measurements
with errors below 1 mm. Furthermore, on-surface measurements demonstrate
robustness when applied in challenging environments with textureless regions.
Overall, by addressing the inherent challenges of image-guided surgery, we lay
the foundation for a more robust and accurate solution for intra- and
postoperative measurements, enabling more precise, safe, and efficient surgical
procedures.
