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.

翻译：图像引导手术中的一个重大挑战是对血管段、切缘或肠道长度等相关结构的精确测量任务。尽管该任务是许多外科手术的关键组成部分，但它涉及大量人力投入且容易产生误差。本文在执业外科医生的指导下，开发了一种基于立体视觉的新型人机协同腹腔镜测量方法。基于整体定性需求分析，本研究提出了一套综合测量方案，整合了诸如RAFT-Stereo和YOLOv8等最先进的机器学习架构。该方法在多种逼真的实验评估环境中进行了验证。结果表明，该方法在距离测量中具有高精度潜力，误差低于1毫米。此外，在无纹理区域等复杂环境下的表面测量也展现出鲁棒性。总体而言，通过解决图像引导手术固有的挑战，我们为更稳健、精确的术中及术后测量方案奠定了基础，从而能够实现更精准、安全且高效的外科手术操作。