arXiv 2410.117032024-10-15cs.ROcs.CV

用于微创手术中多视角图像采集与三维重建的机器人臂平台

Robotic Arm Platform for Multi-View Image Acquisition and 3D Reconstruction in Minimally Invasive Surgery

University College London（伦敦大学学院）

Alexander Saikia, Chiara Di Vece, Sierra Bonilla, Chloe He, Morenike Magbagbeola, Laurent Mennillo, Tobias Czempiel, Sophia Bano, Danail Stoyanov

备注：: 8 pages, 5 figures, 3 tables. This work has been submitted to the IEEE for possible publication

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AI总结：

本文提出一种机器人臂平台，通过适配腹腔镜在不同光照和轨迹下采集多视角图像并结合COLMAP进行三维重建，在微创手术场景中实现了接近亚毫米级的重建精度。

摘要：

微创手术（MIS）具有恢复时间短、患者创伤小等显著优势，但在可视性和操作空间方面存在挑战，这使得精确的三维重建成为手术规划和导航中的重要工具。本工作引入了一个机器人臂平台，用于在微创手术环境中高效地进行多视角图像采集和精确的三维重建。我们将腹腔镜适配到机器人臂上，并在不同光照条件（手术室光照和腹腔镜光照）和不同运动轨迹（球形轨迹和腹腔镜轨迹）下采集了多个羊器官的离体图像。我们采用近期发布的基于学习的特征匹配器并结合COLMAP来生成重建结果。重建结果与高精度激光扫描数据进行对比以进行定量评估。我们的结果表明，虽然在真实微创手术光照和轨迹条件下重建质量最差，但我们的流程的多个版本达到了接近亚毫米级的精度，平均均方根误差为1.05 mm，平均Chamfer距离为0.82 mm。我们最佳的重建结果出现在手术室光照和球形轨迹条件下。我们的机器人平台为微创手术环境中受控、可重复的多视角数据采集提供了工具，我们希望这能促成用于训练基于学习模型的新数据集。

英文摘要：

Minimally invasive surgery (MIS) offers significant benefits such as reduced recovery time and minimised patient trauma, but poses challenges in visibility and access, making accurate 3D reconstruction a significant tool in surgical planning and navigation. This work introduces a robotic arm platform for efficient multi-view image acquisition and precise 3D reconstruction in MIS settings. We adapted a laparoscope to a robotic arm and captured ex-vivo images of several ovine organs across varying lighting conditions (operating room and laparoscopic) and trajectories (spherical and laparoscopic). We employed recently released learning-based feature matchers combined with COLMAP to produce our reconstructions. The reconstructions were evaluated against high-precision laser scans for quantitative evaluation. Our results show that whilst reconstructions suffer most under realistic MIS lighting and trajectory, many versions of our pipeline achieve close to sub-millimetre accuracy with an average of 1.05 mm Root Mean Squared Error and 0.82 mm Chamfer distance. Our best reconstruction results occur with operating room lighting and spherical trajectories. Our robotic platform provides a tool for controlled, repeatable multi-view data acquisition for 3D generation in MIS environments which we hope leads to new datasets for training learning-based models.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。