Lipid membranes and surface reconstruction - a biologically inspired method for 3D segmentation
Nicolas Chiaruttini, University of Geneva

Seminar • 16 May 2017

Abstract
We present a direct 3D surface segmentation method, inspired from lipid membrane physics. Contrary to level-set or mesh-based methods, the segmented surface is defined by a set of independent “lipid” particles that have a position and a normal vector. "Lipids", that are also called surfels, exert a force along their normal vector to adapt to the underlying 3D image (data attachment term). Surface integrity is maintained by local surfels interactions which also allow for topological changes (surface merging or splitting). Segmentation of multiple self-excluding volumes is easily implemented by keeping only repulsive terms between different classes of surfels. We implemented this method into a scriptable ImageJ plugin, and parallelized time critical steps with CUDA. Using a standard desktop computer, we report the segmentation of - 3D tissue from confocal images (~ 800 cells) - human brain surface from MRI sections - and Endoplasmic Reticulum from FIB-SEM data. With a standard desktop computer, this method converges within minutes for ~500k particles. (demo video : https://www.youtube.com/watch?v=TBODq6dVczM).