Semantic-ICP: Iterative Closest Point for Non-rigid Multi-Organ Point Cloud Registration

Abstract

Point cloud registration is important in computer-aided interventions (CAI). While learning-based point cloud registration methods have been developed, their clinical application is hampered by issues of generalizability and explainability. Therefore, classical point cloud registration methods, including Iterative Closest Point (ICP), are still widely applied in CAI. ICP methods fail to consider that: (1) the points have well-defined semantic meaning, in that each point can be related to a specific anatomical label; (2) the deformation required for registration needs to follow biomechanical energy constraints. In this paper, we present a novel non-rigid semantic ICP (SemICP) method that handles multiple point labels and uses linear elastic energy regularization. We use semantic labels to improve the robustness of closest point matching and propose a novel point cloud deformation representation that incorporates explicit biomechanical energy regularization. Our experiments on four datasets show that our method significantly improves the Hausdorff distance and mean surface distance compared with other point cloud registration methods. We also demonstrate that integrating deep learning segmentation models with our registration pipeline enables effective alignment of US and MR point clouds.