Background3D segmentation is often a prerequisite for 3D object display and quantitative measurements. Yet existing voxel-based methods do not directly give information on the object surface or topology. As for spatially continuous approaches such as level-set, active contours and meshes, although providing surfaces and concise shape description, they are generally not suitable for multiple object segmentation and/or for objects with an irregular shape, which can hamper their adoption by bioimage analysts.ResultsWe developed LimeSeg, a computationally efficient and spatially continuous 3D segmentation method. LimeSeg is easy-to-use and can process many and/or highly convoluted objects. Based on the concept of SURFace ELements (“Surfels”), 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 objects of interest.The capabilities of LimeSeg: simultaneous segmentation of numerous non overlapping objects, segmentation of highly convoluted objects and robustness for big datasets are demonstrated on experimental use cases (epithelial cells, brain MRI and FIB-SEM dataset of cellular membrane system respectively).ConclusionIn conclusion, we implemented a new and efficient 3D surface reconstruction plugin adapted for various sources of images, which is deployed in the user-friendly and well-known ImageJ environment.

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.

The activity of the GAP-Biophotonics research group at the University of Geneva in the field of coherent control for discriminating similar biomolecules, such as flavins, proteins and DNA bases, is presented and future developments are discussed.

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