Toward Patient-specific Partial Point Cloud to Surface Completion for Pre- to Intra-operative Registration in Image-guided Liver Interventions
Journal:
arXiv
Published Date:
May 26, 2025
Abstract
Intra-operative data captured during image-guided surgery lacks sub-surface
information, where key regions of interest, such as vessels and tumors, reside.
Image-to-physical registration enables the fusion of pre-operative information
and intra-operative data, typically represented as a point cloud. However, this
registration process struggles due to partial visibility of the intra-operative
point cloud. In this research, we propose a patient-specific point cloud
completion approach to assist with the registration process. Specifically, we
leverage VN-OccNet to generate a complete liver surface from a partial
intra-operative point cloud. The network is trained in a patient-specific
manner, where simulated deformations from the pre-operative model are used to
train the model. First, we conduct an in-depth analysis of VN-OccNet's
rotation-equivariant property and its effectiveness in recovering complete
surfaces from partial intra-operative surfaces. Next, we integrate the
completed intra-operative surface into the Go-ICP registration algorithm to
demonstrate its utility in improving initial rigid registration outcomes. Our
results highlight the promise of this patient-specific completion approach in
mitigating the challenges posed by partial intra-operative visibility. The
rotation equivariant and surface generation capabilities of VN-OccNet hold
strong promise for developing robust registration frameworks for variations of
the intra-operative point cloud.
