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.

基于医生实践指导的立体视觉和机器学习方法，该研究提出一种用于图像引导手术的新型人工智能辅助的腹腔镜测量方法，通过在实验环境中的评估，得到高精度的距离测量，并在具有无纹理区域的挑战性环境中表现出稳健性，为术中和术后测量提供了更精确、安全和高效的解决方案的基础。