构建亚毫米级精度的骨科手术视觉数据集，支持高精度手术导航。
Acquiring Submillimeter-Accurate Multi-Task Vision Datasets for Computer-Assisted Orthopedic Surgery
- 通过三维扫描、视角校准和光学注册三步法生成真实手术场景数据。
- 在猪脊柱实验中实现0.35毫米的三维误差，空间分辨率达0.1毫米。
- 适用于高精度外科视觉重建与特征匹配，适合研究者与临床算法开发。
计算机视觉在无标记手术导航和手术数字化中的应用依赖于高质量的三维真实数据。然而，当前缺乏具备精确三维真实值的可用数据集。本文提出一种针对开放性骨科手术的三维重建与特征匹配数据集生成框架，包含三个核心步骤：三维扫描、高分辨率RGB图像视角校准以及光学注册。在真实手术室环境下对猪脊柱进行脊柱侧弯手术模拟，验证了该方法的有效性。最终实现相对于三维真实值的平均欧氏误差为0.35毫米，生成的手术图像空间分辨率可达0.1毫米。该方法可生成亚毫米级精度的三维真实数据与手术图像，为未来高精度手术视觉任务的数据采集奠定基础。
原文摘要 · Abstract (English)
Advances in computer vision, particularly in optical image-based 3D reconstruction and feature matching, enable applications like marker-less surgical navigation and digitization of surgery. However, their development is hindered by a lack of suitable datasets with 3D ground truth. This work explores an approach to generating realistic and accurate ex vivo datasets tailored for 3D reconstruction and feature matching in open orthopedic surgery. A set of posed images and an accurately registered ground truth surface mesh of the scene are required to develop vision-based 3D reconstruction and matching methods suitable for surgery. We propose a framework consisting of three core steps and compare different methods for each step: 3D scanning, calibration of viewpoints for a set of high-resolution RGB images, and an optical-based method for scene registration. We evaluate each step of this framework on an ex vivo scoliosis surgery using a pig spine, conducted under real operating room conditions. A mean 3D Euclidean error of 0.35 mm is achieved with respect to the 3D ground truth. The proposed method results in submillimeter accurate 3D ground truths and surgical images with a spatial resolution of 0.1 mm. This opens the door to acquiring future surgical datasets for high-precision applications.
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