Overlap-Aware Online-Adaptive Non-Rigid Registration of Intraoperative Tissue in Minimally Invasive Surgery.

Abstract

Non-rigid registration of intraoperative tissue is essential for surgical navigation and scene reconstruction in minimally invasive surgery. However, accurate registration remains challenging due to significant tissue deformation and partial overlaps caused by laparoscope movement. We propose an Overlap-Aware Online-Adaptive Non-Rigid Registration Method (OANRM) to address these challenges. The framework introduces a Hierarchical Matching Network (HMNet) that simultaneously predicts overlapping regions and their correspondences through a novel similarity-based approach. Our method uniquely incorporates an online adaptation mechanism that continuously fine-tunes the network parameters using unsupervised losses, enabling robust performance across varying surgical scenarios without requiring additional training data. A Transform Displacement Deformation Prediction (TDDP) module further enhances the framework by handling non-overlapping regions through integrating Random Sample Consensus with distance-based interpolation. The method is validated on both artificial datasets with controlled deformations and clinical datasets from real surgical procedures. Experimental results demonstrate that OANRM achieves state-of-the-art performance, significantly outperforming existing methods in handling complex tissue deformations and varying overlap ratios. https://github.com/AIGCer0807/OANRM.