#Mesh quality explained registration#
With this aim, visualization relies on registration to obtain the accurate location of structures and objects of interest. This is possible by means of visualization, for example by using stereo-endoscopy, 3D-laparoscopy, and augmented reality (AR). Indeed, providing such information in navigation, i.e., intra-operative image guidance, could assist the surgeon during the intervention. Displaying information about target or risk structures such as the tumor or intra-hepatic vessels and bile ducts can be very helpful. In laparoscopic liver surgery, haptic perception is missing and it is more difficult to localize a tumor. All source code, reference data, models, and evaluation results are openly available for download. Conclusionīy investigating three different factors and improving registration results, we defined a generalizable evaluation pipeline and automatic post-processing strategies that were deemed helpful.
We found that removing non-manifold geometry and noise improved registration performance, and a target surface size of only 16.5% was necessary.
Using the Hausdorff distance, we report a statistical significance for the different partial surfaces. 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). This paper aims to uncover how these affect volume-to-surface registration performance. However, quantifying the impact of target surface size, surface orientation, and mesh quality on non-rigid registration performance remains an open research question. Soft tissue deformation severely impacts the registration of pre- and intra-operative image data during computer-assisted navigation in laparoscopic liver surgery.
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