School of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
Phys Eng Sci Med. 2023 Jun;46(2):719-734. doi: 10.1007/s13246-023-01245-4. Epub 2023 Apr 4.
We propose an algorithm for rigid registration of pre- and intra-operative patient anatomy, represented as pointclouds, during minimally invasive surgery. This capability is essential for development of augmented reality systems for guiding such interventions. Key challenges in this context are differences in the point density in the pre- and intra-operative pointclouds, and potentially low spatial overlap between the two. Solutions, correspondingly, must be robust to both of these phenomena. We formulated a pointclouds registration approach which considers the pointclouds after rigid transformation to be observations of a global non-parametric probabilistic model named Dirichlet Process Gaussian Mixture Model. The registration problem is solved by minimizing the Kullback-Leibler divergence in a variational Bayesian inference framework. By this means, all unknown parameters are recursively inferred, including, importantly, the optimal number of mixture model components, which ensures the model complexity efficiently matches that of the observed data. By presenting the pointclouds as KDTrees, both the data and model are expanded in a coarse-to-fine style. The scanning weight of each point is estimated by its neighborhood, imparting the algorithm with robustness to point density variations. Experiments on several datasets with different levels of noise, outliers and pointcloud overlap show that our method has a comparable accuracy, but higher efficiency than existing Gaussian Mixture Model methods, whose performance is sensitive to the number of model components.
我们提出了一种针对微创手术中患者术前和术中解剖结构（表示为点云）的刚体配准算法。这种能力对于开发用于指导此类干预的增强现实系统至关重要。在这种情况下，关键挑战是术前和术中点云之间的点密度差异，以及两者之间可能的空间重叠较低。相应地，解决方案必须能够稳健地应对这两种现象。我们提出了一种点云配准方法，该方法将刚体变换后的点云视为全局非参数概率模型 Dirichlet 过程高斯混合模型的观测值。通过变分贝叶斯推断框架，通过最小化 Kullback-Leibler 散度来解决配准问题。通过这种方式，可以递归推断出所有未知参数，包括重要的最优混合模型组件数量，从而有效地使模型复杂度与观测数据匹配。通过将点云表示为 KDTrees，数据和模型都以粗到细的方式扩展。通过其邻域来估计每个点的扫描权重，从而使算法具有对点密度变化的鲁棒性。在具有不同噪声水平、异常值和点云重叠的几个数据集上的实验表明，我们的方法具有可比的准确性，但比现有的高斯混合模型方法效率更高，后者的性能对模型组件数量敏感。
