Automated tumor localization via synergistic liver surface and vascular constraints deformation

Open tumor resection is one of the most commonly used treatments for malignant liver tumors. The ability to accurately locate the liver tumor during the operation is the key to the success of the operation. Intraoperative liver tumour localisation remains challenging due to tissue deformation and intraoperative imaging limitations. This paper proposes a dual-constraint framework that synergistically integrates liver surface deformation and vascular biomechanical modeling to resolve this problem. Liver surface registration captures global deformation using a fast finite-element model (18 s), while vascular topology matching refines internal tumor displacement by enforcing correspondence between preoperative and intraoperative vessel trees. This synergistic strategy leverages both external and internal anatomical cues to achieve robust localization. Evaluated on 13 clinical cases, our method achieved sub-millimeter tumor localization accuracy (1.68 ± 0.22 mm). Compared to single-constraint methods (LTLS: 2.04 ± 0.26 mm; LTBV: 2.23 ± 0.31 mm), our approach reduced error by 24%–37% without increasing runtime. This clinically efficient method shows promise for improving intraoperative guidance during liver tumor ablation.