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

Abstract

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.

Cargo crowding drives sorting stringency in COPII vesicles

“…In contrast, the sec13D emp24D strain had fewer single bud profiles and many more multi-budded membranes ( Figure 2B). As a second measure of membrane morphology, we used a quantitative segmentation analysis to measure the sizes of free vesicles (Machado et al, 2019). Maximum diameters of vesicles ranged from 45 nm to 65 nm, with a median of 52 nm ( Figure 2C).…”

“…Vesicles diameter and volume quantifications were obtained with FIJI using the plugin LimeSeg (Machado et al, 2019) as following: the outer contour of a vesicle was selected to the ROI using the "point tool" and "segmented line" tool moving in z through the tomographic slices, adding to the ROI the lowest plane of the vesicle with the point tool, then clicking contours with the segmented line tool every 5 virtual slices and finally closing the volume selecting the top plane of the vesicle with the point tool. LimeSeg Skeleton Segmentation tool settings were adjusted to recognize and segment the outer surface of the vesicle (D_0: 4, F_pressure: 0, Z_scale: 1, Range_in_DO_units: 1, NumberOfIntegrationStep: -1, RealXYPixelSize: 1).…”

Cargo crowding contributes to sorting stringency in COPII vesicles

“…Vesicles diameter and volume quantifications were obtained with FIJI using the plugin LimeSeg (Machado et al, 2019) as follows: the outer contour of a vesicle was selected to the region of interest (ROI) using the "point tool" and "segmented line" tool moving in z through the tomographic slices, adding to the ROI the lowest plane of the vesicle with the point tool, then clicking contours with the segmented line tool every five virtual slices and finally closing the volume selecting the top plane of the vesicle with the point tool. LimeSeg Skeleton Segmentation tool settings were adjusted to recognize and segment the outer surface of the vesicle (D_0: 4, F_pressure: 0, Z_scale: 1, Range_in_DO_units: 1, NumberOfIntegrationStep: −1, RealXYPixelSize: 1).…”

Spindle scaling is governed by cell boundary regulation of microtubule nucleation

“…For cell segmentation, the Fiji plugin LimeSeg was used (59). In brief, at every annotated spindle a sphere (typically with a diameter equal to spindle length) was initialized as a seed for segmentation.…”