3DMMS: robust 3D Membrane Morphological Segmentation of C. elegans embryo

Abstract

Background: Understanding the cellular architecture is a fundamental problem in various biological studies. C. elegans is widely used as a model organism in these studies because of its unique fate determinations. In recent years, researchers have worked extensively on C. elegans to excavate the regulations of genes and proteins on cell mobility and communication. Although various algorithms have been proposed to analyze nucleus, cell shape features are not yet well recorded. This paper proposes a method to systematically analyze three-dimensional morphological cellular features.

Results: Three-dimensional Membrane Morphological Segmentation (3DMMS) makes use of several novel techniques, such as statistical intensity normalization, and region filters, to pre-process the cell images. We then segment membrane stacks based on watershed algorithms. 3DMMS achieves high robustness and precision over different time points (development stages). It is compared with two state-of-the-art algorithms, RACE and BCOMS. Quantitative analysis shows 3DMMS performs best with the average Dice ratio of 97.7% at six time points. In addition, 3DMMS also provides time series of internal and external shape features of C. elegans.

Conclusion: We have developed the 3DMMS based technique for embryonic shape reconstruction at the single-cell level. With cells accurately segmented, 3DMMS makes it possible to study cellular shapes and bridge morphological features and biological expression in embryo research.

Keywords: 3D morphological segmentation; C. elegans; Shape features; Watershed segmentation.

Fig. 1

Dice ratio of 3DMMS, RACE, and BCOMS

Fig. 2

Results comparison. All images come from the same embryo segmentation results. Each column corresponds to the results from the method shown above. Images in the second row are shown in different orientation to images in the first row

Fig. 3

Large gap (cyan arrow) between embryonic surface and eggshell

Fig. 4

Segmentation precision of cells in the embryo. This figure shows the Dice ratio of segmentation results of cells (a) inside and (b) at the boundary of the embryo, respectively. All cells contact the background at t=24,34,44, so they are not showed in (b)

Fig. 5

Comparison between 3DMMS with and without cavity repair

Fig. 6

Morphological deformation of cell “ABala” during division

Fig. 7

Interface matrix between cell “ABala” and its neighboring cells. The sum of each column equals to 1. Every element represents the ratio of the interface between one cell and “ABala”, to the overall interface

Fig. 8

Gap (cyan arrow) between cells inside the embryo

Fig. 9

Flowchart of our methodology

Fig. 10

Slice intensity distribution matrix. a Intensity matrix before adjustment with red threshold line; b Intensity matrix after adjustment with green threshold line. Red line in (a) is also plotted for comparison. Both red and green lines correspond to the same threshold on “Number of points”

Fig. 11

Influence of noise spot and valid membrane region on the EDT of membrane surface. This figure includes steps in region filter. a Largest membrane surface ϕ_max; b Add noise spot ϕ_i to ϕ_max; c EDT of noise and ϕ_max; d Add valid membrane ϕ_i to ϕ_max; e EDT of membrane and ϕ_max. Path (a)-(b)-(c) shows when a noise spot is added into the largest membrane surface, the influenced region R (transparent white mask in (c) and (e)) in the EDT tends to be round. Conversely, Path (a)-(d)-(e) indicates if a valid membrane region is added into the membrane surface, the influenced region has notable polarization. Note that noise spot (yellow in (b)) and valid membrane region (blue in (d)) all exist in binary filtered membrane I^bn, but shown here separately for better demonstration

Fig. 12

Results obtained using the region filter. Results processed by region filter, where blue and yellow regions represent valid membrane signal and noise spots, respectively

Fig. 13

Surface regression on cavity. Binary image (red region in (a)) suffers from lost membrane surface. b is the segmentation results from (a). Two cells are lost because of the background leakage to the embryo. Cavities are repaired with surface regression in (c), preventing background flowing into the background

Fig. 14

A graphical explanation of surface cavity repair. Dot lines represent the distance between segmented embryo surface S^eu and membrane signal I^fm. Pixels with large distance are projected to binary mask R^cavity with positive values

Fig. 15

Comparison between nucleus-centered and membrane-centered watershed segmentation

Fig. 16

Example in division correction a Raw membrane image; b Segmentation before correction; c Segmentation after correction