LimeSeg: a coarse-grained lipid membrane simulation for 3D image segmentation

3D 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 s...

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Bibliographic Details
Published in:BMC bioinformatics 2019-01, Vol.20 (1), p.2-2, Article 2
Main Authors: Machado, Sarah, Mercier, Vincent, Chiaruttini, Nicolas
Format: Article
Language:English
Subjects:
3D segmentation
Bioinformatics
Brain
Cell Membrane - physiology
Cell membranes
Cell surface
Cell volume
Computer graphics
Computer simulation
Diagnostic imaging
Displays (Marketing)
Epithelial cells
Humans
Image processing
Image reconstruction
Image segmentation
ImageJ
Imaging, Three-Dimensional - methods
Insects
Lipid membranes
Lipids
Lipids - physiology
Magnetic resonance imaging
Medical imaging
Membrane lipids
Methods
NMR
Nuclear magnetic resonance
Physiological aspects
Point-cloud
Simulation
Software
Surfel-based
Topology
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Summary:3D 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. We 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). In 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.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-018-2471-0