A case study: impact of target surface mesh size and mesh quality on volume-to-surface registration performance in hepatic soft tissue navigation

Abstract

Purpose: Soft tissue deformation severely impacts the registration of pre- and intra-operative image data during computer-assisted navigation in laparoscopic liver surgery. However, quantifying the impact of target surface size, surface orientation, and mesh quality on non-rigid registration performance remains an open research question. This paper aims to uncover how these affect volume-to-surface registration performance. Methods: To find such evidence, we design three experiments that are evaluated using a three-step pipeline: (1) volume-to-surface registration using the physics-based shape matching method or PBSM, (2) voxelization of the deformed surface to a 1024 3 voxel grid, and (3) computation of similarity (e.g., mutual information), distance (i.e., Hausdorff distance), and classical metrics (i.e., mean squared error or MSE). Results: Using the Hausdorff distance, we report a statistical significance for the different partial surfaces. We found that removing non-manifold geometry and noise improved registration performance, and a target surface size of only 16.5% was necessary. Conclusion: By investigating three different factors and improving registration results, we defined a generalizable evaluation pipeline and automatic post-processing strategies that were deemed helpful. All source code, reference data, models, and evaluation results are openly available for download: https://github.com/ghattab/EvalPBSM/.