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Biologically constrained optimization based cell membrane segmentation in C. elegans embryos
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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5477254/ https://www.ncbi.nlm.nih.gov/pubmed/28629355 http://dx.doi.org/10.1186/s12859-017-1717-6 |
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author | Azuma, Yusuke Onami, Shuichi |
author_facet | Azuma, Yusuke Onami, Shuichi |
author_sort | Azuma, Yusuke |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1717-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5477254 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54772542017-06-23 Biologically constrained optimization based cell membrane segmentation in C. elegans embryos Azuma, Yusuke Onami, Shuichi BMC Bioinformatics Methodology Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1717-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-19 /pmc/articles/PMC5477254/ /pubmed/28629355 http://dx.doi.org/10.1186/s12859-017-1717-6 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Azuma, Yusuke Onami, Shuichi Biologically constrained optimization based cell membrane segmentation in C. elegans embryos |
title | Biologically constrained optimization based cell membrane segmentation in C. elegans embryos |
title_full | Biologically constrained optimization based cell membrane segmentation in C. elegans embryos |
title_fullStr | Biologically constrained optimization based cell membrane segmentation in C. elegans embryos |
title_full_unstemmed | Biologically constrained optimization based cell membrane segmentation in C. elegans embryos |
title_short | Biologically constrained optimization based cell membrane segmentation in C. elegans embryos |
title_sort | biologically constrained optimization based cell membrane segmentation in c. elegans embryos |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5477254/ https://www.ncbi.nlm.nih.gov/pubmed/28629355 http://dx.doi.org/10.1186/s12859-017-1717-6 |
work_keys_str_mv | AT azumayusuke biologicallyconstrainedoptimizationbasedcellmembranesegmentationincelegansembryos AT onamishuichi biologicallyconstrainedoptimizationbasedcellmembranesegmentationincelegansembryos |