Distance-aware smoothing of surface meshes for surgical planning

Abstract

The evaluation of spatial relationships between anatomic structures is a major task in surgical planning. Surface models generated from medical image data (intensity, binary) are often used for visualization and 3D measurement of extents and distances between neighboring structures. In applications for intervention or radiation treatment planning, the surface models need to exhibit a natural look (referring to smoothness of the surface), but also to be accurate. Smoothing algorithms allow to reduce artifacts from mesh generation, but the result is always a tradeoff between smoothness and accuracy. Required features will be removed and distances between adjacent structures get changed. Thus, we present a modification to common mesh smoothing algorithms, which allows to generate smooth surfaces models while distances of neighboring structures are preserved. We compared our distance-aware approach to conventional uniform smoothing methods and evaluated the resulting surface models regarding smoothness and accuracy for their application within the context of surgical planning.