FoundationPose-Initialized 3D-2D Liver Registration for Surgical Augmented Reality

• URL: http://arxiv.org/abs/2602.17517v1
• Date: Thu, 19 Feb 2026 16:31:14 GMT
• Title: FoundationPose-Initialized 3D-2D Liver Registration for Surgical Augmented Reality
• Authors: Hanyuan Zhang, Lucas He, Runlong He, Abdolrahim Kadkhodamohammadi, Danail Stoyanov, Brian R. Davidson, Evangelos B. Mazomenos, Matthew J. Clarkson,
• Abstract summary: We integrate laparoscopic depth maps with a foundation pose estimator for camera-liver pose estimation.<n>On real patient data, the depth-augmented foundation pose approach achieved 9.91 mm mean registration error in 3 cases.<n>This pipeline achieves clinically relevant accuracy while offering a lightweight, engineering-friendly alternative to FE-based deformation.
• Score: 14.330143515294928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Augmented reality can improve tumor localization in laparoscopic liver surgery. Existing registration pipelines typically depend on organ contours; deformable (non-rigid) alignment is often handled with finite-element (FE) models coupled to dimensionality-reduction or machine-learning components. We integrate laparoscopic depth maps with a foundation pose estimator for camera-liver pose estimation and replace FE-based deformation with non-rigid iterative closest point (NICP) to lower engineering/modeling complexity and expertise requirements. On real patient data, the depth-augmented foundation pose approach achieved 9.91 mm mean registration error in 3 cases. Combined rigid-NICP registration outperformed rigid-only registration, demonstrating NICP as an efficient substitute for finite-element deformable models. This pipeline achieves clinically relevant accuracy while offering a lightweight, engineering-friendly alternative to FE-based deformation.

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