Temporal Depth Fusion-Based Markerless Registration for Deformable Soft-Tissue Tracking in Augmented Reality Surgical Navigation
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Abstract
Accurate registration of deformable soft tissues remains a major challenge in markerless augmented reality (AR)-guided surgery due to non-rigid deformation and depth sensing noise. This study proposes a temporal depth fusion framework that integrates multi-frame depth accumulation with biomechanical deformation priors for real-time soft-tissue registration. A spatio-temporal filtering module first reduces depth variance using sliding-window Bayesian fusion across 30 consecutive frames. A physics-constrained non-rigid iterative closest point (NR-ICP) algorithm is then applied with a finite element–based regularization term to maintain anatomical plausibility. Experiments were conducted on 24 ex vivo porcine liver datasets and 12 human phantom simulations (total 36 cases). Average target registration error (TRE) was reduced from 3.42 mm (standard ICP) to 1.78 mm. Depth noise variance decreased by 37.6%, and tracking latency remained below 28 ms on a head-mounted AR device. The results demonstrate that temporally fused depth data significantly improves markerless soft-tissue alignment accuracy while preserving real-time performance.
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