SSBD:database

Detail of Membrane

Yusuke Azuma, Shuichi Onami (2017) Biologically constrained optimization based cell membrane segmentation in C. elegans embryos., BMC bioinformatics, Volume 18, Number 1, pp. 307

Published in 2017 Jun 19 (Electronic publication in June 19, 2017, midnight )

• doi: 10.1186/s12859-017-1717-6
• pmid: 28629355
  
• url: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1717-6
  

(Abstract) BACKGROUND: Recent 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. RESULTS: Our 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. CONCLUSIONS: BCOMS 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.

• Algorithms[major]
• Animals
• Automation
• Caenorhabditis elegans/growth & development[major]
• Caenorhabditis elegans/physiology
• Cell Membrane/chemistry
• Cell Membrane/metabolism[major]
• Embryo, Nonmammalian/physiology
• Embryonic Development
• Image Processing, Computer-Assisted/methods[major]