LimeSeg: A coarsed-grained lipid membrane simulation for 3D image segmentation

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.

Of Cell Shapes and Motion: The Physical Basis of Animal Cell Migration

“…Here, a deformable representation of the cell contour deforms, typically using the information of the intensity of the cell membrane. Active contour methods are well established to analyze 2D cell shapes (Ray et al, 2002), and within the last few years, 3D versions have been developed (Machado et al, 2019;Smith et al, 2017). While thresholding and active contour approaches can be implemented easily, manual detection of cell shapes is often out-performing them.…”

Spindle Scaling Is Governed by Cell Boundary Regulation of Microtubule Nucleation

“…For cell segmentation, the Fiji plugin LimeSeg was used [63]. In brief, at every annotated spindle a sphere (typically with a diameter equal to spindle length) was initialized as a seed for segmentation.…”

Transient active osmotic swelling of epithelium upon curvature induction

“…To remove cells from the Z-stack edges that may have been partly cut, nuclei which position was equal or lower than 7μm from the image border were removed from the statistics. The centroid positions were then used as seeds for the segmentation of Z-Stacks of MDCK-II cells labelled with CellMask or Myr-Palm-GFP, and using LimeSeg 42 plugin on FijiJ.…”

“…We used MDCK stably expressing the 6 nucleus marker H2B-RFP and the plasma membrane marker Myr-Lyn-GFP 41 or MDCK labelled with the plasma membrane marker CellMask and the nuclear stain Hoechst (see Methods). To analyze their shape, we segmented cells using Limeseg, a 3D cell segmentation ImageJ plugin 42 , and we quantified the average cell volume over time, both on planar and tubular regions ( Fig. 2a).…”