Massalimova Aidana, Liebmann Florentin, Jecklin Sascha, Carrillo Fabio, Farshad Mazda, Fürnstahl Philipp
Research in Orthopedic Computer Science, University Hospital Balgrist, University of Zurich, Zurich, Switzerland.
Department of Orthopaedics, University Hospital Balgrist, University of Zurich, Zurich, Switzerland.
Comput Assist Surg (Abingdon). 2025 Dec;30(1):2511126. doi: 10.1080/24699322.2025.2511126. Epub 2025 Jun 3.
State-of-the-art computer- and robot-assisted surgery systems rely on intraoperative imaging technologies such as computed tomography and fluoroscopy to provide detailed 3D visualizations of patient anatomy. However, these methods expose both patients and clinicians to ionizing radiation. This study introduces a radiation-free approach for 3D spine reconstruction using RGB-D data. Inspired by the "mental map" surgeons form during procedures, we present SurgPointTransformer, a shape completion method that reconstructs unexposed spinal regions from sparse surface observations. The method begins with a vertebra segmentation step that extracts vertebra-level point clouds for subsequent shape completion. SurgPointTransformer then uses an attention mechanism to learn the relationship between visible surface features and the complete spine structure. The approach is evaluated on an dataset comprising nine samples, with CT-derived data used as ground truth. SurgPointTransformer significantly outperforms state-of-the-art baselines, achieving a Chamfer distance of 5.39 mm, an F-score of 0.85, an Earth mover's distance of 11.00 and a signal-to-noise ratio of 22.90 dB. These results demonstrate the potential of our method to reconstruct 3D vertebral shapes without exposing patients to ionizing radiation. This work contributes to the advancement of computer-aided and robot-assisted surgery by enhancing system perception and intelligence.
最先进的计算机辅助和机器人辅助手术系统依赖于计算机断层扫描和荧光透视等术中成像技术，以提供患者解剖结构的详细三维可视化图像。然而，这些方法会使患者和临床医生暴露于电离辐射中。本研究介绍了一种使用RGB-D数据进行三维脊柱重建的无辐射方法。受外科医生在手术过程中形成的“心理地图”启发，我们提出了SurgPointTransformer，这是一种形状补全方法，可从稀疏的表面观测中重建未暴露的脊柱区域。该方法首先进行椎体分割步骤，提取椎体级别的点云，以便后续进行形状补全。然后，SurgPointTransformer使用注意力机制来学习可见表面特征与完整脊柱结构之间的关系。该方法在一个包含九个样本的数据集上进行了评估，使用CT衍生数据作为基准真值。SurgPointTransformer显著优于最先进的基线方法，其倒角距离为5.39毫米，F分数为0.85，推土机距离为11.00，信噪比为22.90分贝。这些结果证明了我们的方法在不使患者暴露于电离辐射的情况下重建三维椎体形状的潜力。这项工作通过增强系统感知和智能，为计算机辅助和机器人辅助手术的发展做出了贡献。
