Open Access

Nicolas Klenert, Patricia Loba-Gómez, Leiss Abdal Al, Juan Diego Gallego Nicolás, Harold Phelippeau, Rachida Seghiri, Antonio Martinez-Sanchez, Daniel Baum

• The geometric reconstruction of biological membranes from electron microscopy data, such as three-dimensional images acquired by cryo-electron tomography, is of great importance for the analysis of cellular environments. Recent advances in deep learning-based approaches enable the segmentation of biological membranes in those data with unprecedented quality and completeness, resulting in highly complex morphological structures. However, in order to fully understand the geometric properties of membranes and how they relate to other cellular structures, including membrane proteins, an explicit geometric representation in the form of triangle meshes is necessary. Here, we present a ridge surface-based approach that is able to transform a wide variety of membrane morphologies, given as segmented voxel representations of membranes, into high-quality triangle meshes. We compare our approach with MidSurfer, a recently published method, which not only fails for those complex morphologies but is also at least one order of magnitude slower than the presented approach. In addition, we test a crease surface extraction algorithm and a medial surface skeleton extraction method on the data presented, and discuss the pros and cons of each approach.