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

Abstract

Background: 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.

Results: 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).

Conclusion: 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.

Keywords: 3D segmentation; Cell membrane segmentation; Cell surface; Cell volume; ImageJ; Point-cloud; Surfel-based.

Fig. 1

Surfel interaction rules. a - Forces and torque acting on a neighboring pair of surfel. Top left: notation convention for the position and normal vector of surfels. Top right: preferred distance interaction d₀ and associated force F_dist. Bottom left, planar interaction F_plane. Bottom right, T_tilt. b - Interaction with the 3D image. F_signal has a constant norm. It is positive, null, or negative depending on the local image maximum. c - F_pressure exerted along the normal vector. The sign of f_pressure controls surface shrinkage or expansion. d - Adaptation of surfel number depending on local neighbors. The number of neighbors within the sphere of influence is counted. Depending on this number, the surfel is removed or a new one is generated

Fig. 2

Point cloud mechanics characterization. a - Behavior of surfels network with fixed d₀ as a function of f_pressure when it encounters a circular hole of radius r_hole. In the blue region, the surfel mesh does not cross the hole. In the yellow region the surfel mesh flows through the hole. A 1/r dotted line approximates the frontier between these regions. b - Image noise segmentation benchmark, see text for details. Left: equatorial plane of sphere image with various noises and resulting segmentation. Right: Segmentation score (i.e. root mean square of surfel distance to the target sphere in pixel) as a function as noise and f_pressure, all other parameters are unchanged. c - Surface fusion test. The initial state consists of a spherical seed inside a torus. After several iterations, the shape of the segmentation surface successfully merges with itself (f_pressure>0). d - Surface fission test. The initial state consists of a spherical seed surrounding two spherical objects. The segmentation surface successfully splits during the course of the segmentation (f_pressure<0)

Fig. 3

Segmentation of deformed lipid vesicles. The two vesicles are segmented sequentially. Right: segmentation outcome. Three z slices where surfels appear as dots are shown as well as the 3D reconstruction, where the in-planes surfels are highlighted

Fig. 4

Human brain MRI surface segmentation. From left to right: 1 - initialization of the shape with ROI skeleton (blue line on the data image). 2 - After segmentation convergence with d₀=4, many details of the cortex are missed. 3 - Segmentation refinement by decreasing d₀ to 1.5. 4 - Zooms showing details being retrieved by the finest segmentation where surfels appear as green dots

Fig. 5

Endoplasmic reticulum (ER) and plasma membrane (PM) segmentation of a FIB-SEM HeLa cell dataset. a - Typical data slice where the nucleus, ER and PM are visible. b - Resulting segmentation of ER (magenta) and PM (green). c - Segmented ER and PM, overlaid on the original data. d - Missed parts of nuclear envelope where the double membrane is too thin to be correctly segmented (left). Spurious hole generated during segmentation (right). e - Detail showing plasma membrane invagination in 2D and 3D. F - Detail of nuclear pore complex as seen on 2D and on 3D. Scalebars: a, c: 1μm; d, e, f: 100nm

Fig. 6

Drosophila egg chamber segmentation. a - Segmentation of the follicle cells. Up: surfels appear as colored dot (one color per cell). Below: 3D reconstruction. Only the surfels below the shown slice on top are represented. Left: initial state; right: final state. b – Segmentation of the nurse cells. During the course of segmentation, surfels of follicle cells were locked to maintain the egg outline. c – 3D reconstruction output for nurse cells and follicle cells. d – Detail of surfel positions after segmentation convergence