基于椭圆轴估计的圆柱体CAD到CT配准
CAD-to-CT Registration of Cylindrical Objects via Ellipse-Based Axis Estimation
AI总结：
提出一种两阶段几何配准方法，通过检测CT切片中的椭圆截面估计旋转轴，再通过体素化CAD模型并最大化与CT扫描的体积重叠实现圆柱体（电离室）的精确配准，无需强度校准或特征匹配，倾斜和方向误差低于0.1°。
摘要：
CAD模型与CT扫描的精确配准对于在体积成像中建立真实几何基准至关重要。获取可靠的对象掩膜在机器学习环境中日益重要；随着最新架构能力增强，需要大规模数据集以充分利用其能力。当CT灰度值缺乏校准参考时，传统的基于强度的方法失效，而基于点的算法（如ICP、RANSAC）需要理想化CAD几何与噪声体积CT数据之间不可用的特征对应。我们提出了一种针对圆柱体（电离室）的两阶段几何配准方法，利用对象的独特几何特征。首先，通过检测CT切片中的椭圆截面、对边缘检测轮廓拟合椭圆，并在RANSAC异常值去除后对拟合椭圆中心进行PCA，来估计3D旋转轴。其次，将CAD模型体素化，沿检测轴定向，并通过平移调整最大化与CT扫描的体积重叠。该方法无需强度校准或特征匹配，即可实现倾斜和方向误差低于0.1°的鲁棒配准。配准后，对齐的CAD模型为机器学习目标定位和工业CT工作流中的自动分析等应用提供真实几何基准。
英文摘要：
Accurate registration of CAD models to CT scans is essential for establishing ground truth geometry in volumetric imaging. Obtaining reliable object masks is of growing importance in machine learning settings; as recent architectures grow more capable, huge datasets are required to fully utilise their capabilities. Traditional intensity-based methods fail when CT grayscale values lack calibration references, while point-based algorithms (e.g., ICP, RANSAC) require feature correspondence unavailable between idealized CAD geometry and noisy volumetric CT data. We propose a two-stage geometric registration method for cylindrical objects (ionization chambers) that takes advantage of the distinctive geometric features of the objects. First, we estimate the 3D rotation axis by detecting elliptical cross-sections across CT slices, fitting ellipses to edge-detected contours, and performing PCA on the fitted ellipse centers after RANSAC outlier removal. Second, we voxelize the CAD model, orient it along the detected axis, and maximize volumetric overlap with the CT scan through translational adjustment. This approach achieves robust registration with tilt and orientation errors below $0.1^\circ$ without intensity calibration or feature matching. Once registered, the aligned CAD model provides ground truth geometry for applications including machine learning-based object localization and automated analysis in industrial CT workflows.
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。
