Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection

-- 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 intraopera-tive liver visual information in shape deformation modeling. 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. Self-P2IR 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. To facilitate the validation of the registration performance, we also construct an in-vivo registration dataset containing liver resection videos of 21 patients, called 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. The code and dataset are available at Self-P2IR. Jun Zhou, Bingchen Gao, and Jing Qin are with the Center of Smart Health, School of Nursing, The Hong Kong Polytechnic University, HKSAR, China. Jialun Pei and Pheng-Ann Heng are with the Department of Computer Science and Engineering, Pheng-Ann Heng is also with the Institute of Medical Intelligence and XR, The Chinese University of Hong Kong, HK-SAR, China. Kai Wang is with the Division of Hepatobiliopancreatic Surgery, Department of General Surgery, Nanfang Hospital, Guangzhou, China. Jun Zhou and Bingchen Gao contributed equally . Precise imaging guidance to determine the relative position between the tumor and major vessels can enhance the success of liver resection and reduce the risk of postoperative complications [1], [2].