Göbel Birthe, Reiterer Alexander, Möller Knut
Department of Sustainable Systems Engineering-INATECH, University of Freiburg, Emmy-Noether-Street 2, 79110 Freiburg im Breisgau, Germany.
KARL STORZ SE & Co. KG, Dr.-Karl-Storz-Street 34, 78532 Tuttlingen, Germany.
J Imaging. 2024 Jul 24;10(8):180. doi: 10.3390/jimaging10080180.
Image-based 3D reconstruction enables laparoscopic applications as image-guided navigation and (autonomous) robot-assisted interventions, which require a high accuracy. The review's purpose is to present the accuracy of different techniques to label the most promising. A systematic literature search with PubMed and google scholar from 2015 to 2023 was applied by following the framework of "Review articles: purpose, process, and structure". Articles were considered when presenting a quantitative evaluation (root mean squared error and mean absolute error) of the reconstruction error (Euclidean distance between real and reconstructed surface). The search provides 995 articles, which were reduced to 48 articles after applying exclusion criteria. From these, a reconstruction error data set could be generated for the techniques of stereo vision, Shape-from-Motion, Simultaneous Localization and Mapping, deep-learning, and structured light. The reconstruction error varies from below one millimeter to higher than ten millimeters-with deep-learning and Simultaneous Localization and Mapping delivering the best results under intraoperative conditions. The high variance emerges from different experimental conditions. In conclusion, submillimeter accuracy is challenging, but promising image-based 3D reconstruction techniques could be identified. For future research, we recommend computing the reconstruction error for comparison purposes and use ex/in vivo organs as reference objects for realistic experiments.
基于图像的三维重建可实现腹腔镜应用，如图像引导导航和（自主）机器人辅助干预，这些应用需要高精度。本综述的目的是展示不同技术的准确性，以找出最有前景的技术。按照“综述文章：目的、过程和结构”的框架，使用PubMed和谷歌学术对2015年至2023年的文献进行了系统检索。当文章呈现重建误差（真实表面与重建表面之间的欧几里得距离）的定量评估（均方根误差和平均绝对误差）时，将其纳入考虑。检索共得到995篇文章，应用排除标准后减少至48篇。从中可以为立体视觉、运动形状、同步定位与地图构建、深度学习和结构光等技术生成一个重建误差数据集。重建误差从低于一毫米到高于十毫米不等，其中深度学习和同步定位与地图构建在术中条件下取得了最佳结果。这种高方差源于不同的实验条件。总之，亚毫米级精度具有挑战性，但可以识别出有前景的基于图像的三维重建技术。对于未来的研究，我们建议计算重建误差以进行比较，并使用体内/体外器官作为真实实验的参考对象。
