Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection
This addresses a critical challenge in laparoscopic liver resection surgery by improving registration accuracy for surgeons, though it appears incremental as it builds on existing registration workflows with novel components.
The paper tackles the problem of registering preoperative 3D liver models to intraoperative 2D laparoscopic frames without relying on anatomical landmarks, which often have ambiguous definitions and insufficient integration of visual information. The proposed landmark-free framework achieves superior performance in experiments on synthetic and in-vivo datasets, including a new dataset of 21 patients with 346 keyframes.
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.