Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection
Journal:
arXiv
Published Date:
Apr 21, 2025
Abstract
Liver registration by overlaying preoperative 3D models onto intraoperative
2D frames can assist surgeons in perceiving the spatial anatomy of the liver
clearly for a higher surgical success rate. Existing registration methods rely
heavily on anatomical landmark-based workflows, which encounter two major
limitations: 1) ambiguous landmark definitions fail to provide efficient
markers for registration; 2) insufficient integration of intraoperative liver
visual information in shape deformation modeling. To address these challenges,
in this paper, we propose a landmark-free preoperative-to-intraoperative
registration framework utilizing effective self-supervised learning, termed
\ourmodel. This framework transforms the conventional 3D-2D workflow into a
3D-3D registration pipeline, which is then decoupled into rigid and non-rigid
registration subtasks. \ourmodel~first introduces a feature-disentangled
transformer to learn robust correspondences for recovering rigid
transformations. Further, a structure-regularized deformation network is
designed to adjust the preoperative model to align with the intraoperative
liver surface. This network captures structural correlations through geometry
similarity modeling in a low-rank transformer network. To facilitate the
validation of the registration performance, we also construct an in-vivo
registration dataset containing liver resection videos of 21 patients, called
\emph{P2I-LReg}, which contains 346 keyframes that provide a global view of the
liver together with liver mask annotations and calibrated camera intrinsic
parameters. Extensive experiments and user studies on both synthetic and
in-vivo datasets demonstrate the superiority and potential clinical
applicability of our method.
