Abstract
Bioimage analysis is an important preliminary step required for data representation and quantitative studies. To carry out these tasks, we developed LimeSeg, an easy-to-use, efficient and modular 3D image segmentation method. Based on the idea of SURFace ELements, LimeSeg resembles a highly coarse-grained simulation of a lipid membrane in which a set of particles, analogous to lipid molecules, are attracted to local image maxima. The particles are self-generating and self-destructing thus providing the ability for the membrane to evolve towards the contour of the object of interest. We characterize the emergent mechanical properties of this system and show how it can be used to segment many 3D objects from numerous types of image of biological samples (brain MRI, cell epithelium, cellular organelles). LimeSeg is available as a Fiji plugin that includes simple commands, a 3D visualizer, and customization options via ImageJ scripting.
