ArticleBMC bioinformatics2017
Biologically constrained optimization based cell membrane segmentation in C. elegans embryos.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- EmbSAM: cell boundary localization and Segment Anything Model for fast images of developing embryos.Communications biology · 2025Article
- Cell lineage-resolved embryonic morphological map reveals signaling associated with cell fate and size asymmetry.Nature communications · 2025Article
- CShaperApp: Segmenting and analyzing cellular morphologies of the developingQuantitative biology (Beijing, China) · 2024Article
- Nfinder: automatic inference of cell neighborhood in 2D and 3D using nuclear markers.BMC bioinformatics · 2023Article
- An ImageJ-based tool for three-dimensional registration between different types of microscopic images.Development, growth & differentiation · 2023Article
- Systematic analysis of cell morphodynamics inFrontiers in bioinformatics · 2023Article
- Visualizing and quantifying molecular and cellular processes in Caenorhabditis elegans using light microscopy.Genetics · 2022Review
- 3D cell morphology detection by association for embryo heart morphogenesis.Biological imaging · 2022Article
- Delineating the mechanisms and design principles ofComputational and structural biotechnology journal · 2022Review
- Automated phenotyping ofMicrosystems & nanoengineering · 2020Article
- Improved 3D cellular morphometry of Caenorhabditis elegans embryos using a refractive index matching medium.PloS one · 2020Article
- Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates.Computational and structural biotechnology journal · 2020Review
- 3DMMS: robust 3D Membrane Morphological Segmentation of C. elegans embryo.BMC bioinformatics · 2019Article
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2 authors.
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Abstract
backgroundRecent advances in bioimaging and automated analysis methods have enabled the large-scale systematic analysis of cellular dynamics during the embryonic development of Caenorhabditis elegans. Most of these analyses have focused on cell lineage tracing rather than cell shape dynamics. Cell shape analysis requires cell membrane segmentation, which is challenging because of insufficient resolution and image quality. This problem is currently solved by complicated segmentation methods requiring laborious and time consuming parameter adjustments.
resultsOur new framework BCOMS (Biologically Constrained Optimization based cell Membrane Segmentation) automates the extraction of the cell shape of C. elegans embryos. Both the segmentation and evaluation processes are automated. To automate the evaluation, we solve an optimization problem under biological constraints. The performance of BCOMS was validated against a manually created ground truth of the 24-cell stage embryo. The average deviation of 25 cell shape features was 5.6%. The deviation was mainly caused by membranes parallel to the focal planes, which either contact the surfaces of adjacent cells or make no contact with other cells. Because segmentation of these membranes was difficult even by manual inspection, the automated segmentation was sufficiently accurate for cell shape analysis. As the number of manually created ground truths is necessarily limited, we compared the segmentation results between two adjacent time points. Across all cells and all cell cycles, the average deviation of the 25 cell shape features was 4.3%, smaller than that between the automated segmentation result and ground truth.
conclusionsBCOMS automated the accurate extraction of cell shapes in developing C. elegans embryos. By replacing image processing parameters with easily adjustable biological constraints, BCOMS provides a user-friendly framework. The framework is also applicable to other model organisms. Creating the biological constraints is a critical step requiring collaboration between an experimentalist and a software developer.
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