We propose an anatomically-informed initialisation method for interpatient CT non-rigid registration (NRR), using a learning-based model to estimate correspondences between organ structures. A thin plate spline (TPS) deformation, set up using the correspondence predictions, is used to initialise the scans before a second NRR step. We compare two established NRR methods for the second step: a B-spline iterative optimisation-based algorithm and a deep learning-based approach. Registration performance is evaluated with and without the initialisation by assessing the similarity of propagated structures. Our proposed initialisation improved the registration performance of the learning-based method to more closely match the traditional iterative algorithm, with the mean distance-to-agreement reduced by 1.8mm for structures included in the TPS and 0.6mm for structures not included, while maintaining a substantial speed advantage (5 vs. 72 seconds).

翻译：我们提出了一种基于解剖学信息的初始化方法，用于患者间CT非刚性配准，该方法利用基于学习的模型来估计器官结构之间的对应关系。使用对应关系预测建立的薄板样条变形在第二次非刚性配准步骤之前用于初始化扫描。我们比较了两种成熟的非刚性配准方法作为第二步：一种基于B样条迭代优化的算法和一种基于深度学习的方法。通过评估传播结构的相似性，在有和没有初始化的情况下评估配准性能。我们提出的初始化方法提高了基于学习方法的配准性能，使其更接近传统的迭代算法，对于包含在薄板样条中的结构，平均一致距离减少了1.8毫米，对于未包含的结构减少了0.6毫米，同时保持了显著的速度优势（5秒对72秒）。