Semantic-ICP: Iterative Closest Point for Non-rigid Multi-Organ Point Cloud Registration
Journal:
arXiv
Published Date:
Mar 2, 2025
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, such as
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 needs to follow biomechanical energy constraints. In this paper, we
present a novel semantic ICP (sem-ICP) method that handles multiple point
labels and uses linear elastic energy regularization. We use semantic labels to
improve the robustness of the closest point matching and propose a new point
cloud deformation representation to apply explicit biomechanical energy
regularization. Our experiments on the Learn2reg abdominal MR-CT registration
dataset and a trans-oral robotic surgery ultrasound-CT registration dataset
show that our method improves the Hausdorff distance compared with other
state-of-the-art ICP-based registration methods. We also perform a sensitivity
study to show that our rigid initialization achieves better convergence with
different initializations and visible ratios.
