Abstract
BackgroundUnderstanding 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.ResultsThree-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.ConclusionWe 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.
Highlights
Understanding the cellular architecture is a fundamental problem in various biological studies
This paper develops a method for 3D Membranebased Morphological Segmentation (3DMMS) to extract cell-level embryonic shapes
Segmentation results from 3DMMS were quantitatively evaluated and compared with two state-of-the-art methods, RACE and BCOMS
Summary
Understanding the cellular architecture is a fundamental problem in various biological studies. Researchers have worked extensively on C. elegans to excavate the regulations of genes and proteins on cell mobility and communication. 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. Advanced imaging technologies provide the biologist with considerable insight into the micro-sized embryo, and extend the possibility to conduct research at single-cell level. Manually analyzing countless cell images is tedious and time-consuming. Automatic image processing becomes essential for exploiting spatiotemporal cellular features [1]. Considerable researches on nuclei stack images promote the formulation of biological theories related to nuclear shape and location [2,3,4].
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