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ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes
The quantification of cell shape, cell migration, and cell rearrangements is important for addressing classical questions in developmental biology such as patterning and tissue morphogenesis. Time-lapse microscopic imaging of transgenic embryos expressing fluorescent reporters is the method of choic...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3516542/ https://www.ncbi.nlm.nih.gov/pubmed/23236265 http://dx.doi.org/10.1371/journal.pcbi.1002780 |
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author | Mosaliganti, Kishore R. Noche, Ramil R. Xiong, Fengzhu Swinburne, Ian A. Megason, Sean G. |
author_facet | Mosaliganti, Kishore R. Noche, Ramil R. Xiong, Fengzhu Swinburne, Ian A. Megason, Sean G. |
author_sort | Mosaliganti, Kishore R. |
collection | PubMed |
description | The quantification of cell shape, cell migration, and cell rearrangements is important for addressing classical questions in developmental biology such as patterning and tissue morphogenesis. Time-lapse microscopic imaging of transgenic embryos expressing fluorescent reporters is the method of choice for tracking morphogenetic changes and establishing cell lineages and fate maps in vivo. However, the manual steps involved in curating thousands of putative cell segmentations have been a major bottleneck in the application of these technologies especially for cell membranes. Segmentation of cell membranes while more difficult than nuclear segmentation is necessary for quantifying the relations between changes in cell morphology and morphogenesis. We present a novel and fully automated method to first reconstruct membrane signals and then segment out cells from 3D membrane images even in dense tissues. The approach has three stages: 1) detection of local membrane planes, 2) voting to fill structural gaps, and 3) region segmentation. We demonstrate the superior performance of the algorithms quantitatively on time-lapse confocal and two-photon images of zebrafish neuroectoderm and paraxial mesoderm by comparing its results with those derived from human inspection. We also compared with synthetic microscopic images generated by simulating the process of imaging with fluorescent reporters under varying conditions of noise. Both the over-segmentation and under-segmentation percentages of our method are around 5%. The volume overlap of individual cells, compared to expert manual segmentation, is consistently over 84%. By using our software (ACME) to study somite formation, we were able to segment touching cells with high accuracy and reliably quantify changes in morphogenetic parameters such as cell shape and size, and the arrangement of epithelial and mesenchymal cells. Our software has been developed and tested on Windows, Mac, and Linux platforms and is available publicly under an open source BSD license (https://github.com/krm15/ACME). |
format | Online Article Text |
id | pubmed-3516542 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-35165422012-12-12 ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes Mosaliganti, Kishore R. Noche, Ramil R. Xiong, Fengzhu Swinburne, Ian A. Megason, Sean G. PLoS Comput Biol Research Article The quantification of cell shape, cell migration, and cell rearrangements is important for addressing classical questions in developmental biology such as patterning and tissue morphogenesis. Time-lapse microscopic imaging of transgenic embryos expressing fluorescent reporters is the method of choice for tracking morphogenetic changes and establishing cell lineages and fate maps in vivo. However, the manual steps involved in curating thousands of putative cell segmentations have been a major bottleneck in the application of these technologies especially for cell membranes. Segmentation of cell membranes while more difficult than nuclear segmentation is necessary for quantifying the relations between changes in cell morphology and morphogenesis. We present a novel and fully automated method to first reconstruct membrane signals and then segment out cells from 3D membrane images even in dense tissues. The approach has three stages: 1) detection of local membrane planes, 2) voting to fill structural gaps, and 3) region segmentation. We demonstrate the superior performance of the algorithms quantitatively on time-lapse confocal and two-photon images of zebrafish neuroectoderm and paraxial mesoderm by comparing its results with those derived from human inspection. We also compared with synthetic microscopic images generated by simulating the process of imaging with fluorescent reporters under varying conditions of noise. Both the over-segmentation and under-segmentation percentages of our method are around 5%. The volume overlap of individual cells, compared to expert manual segmentation, is consistently over 84%. By using our software (ACME) to study somite formation, we were able to segment touching cells with high accuracy and reliably quantify changes in morphogenetic parameters such as cell shape and size, and the arrangement of epithelial and mesenchymal cells. Our software has been developed and tested on Windows, Mac, and Linux platforms and is available publicly under an open source BSD license (https://github.com/krm15/ACME). Public Library of Science 2012-12-06 /pmc/articles/PMC3516542/ /pubmed/23236265 http://dx.doi.org/10.1371/journal.pcbi.1002780 Text en © 2012 Mosaliganti et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Mosaliganti, Kishore R. Noche, Ramil R. Xiong, Fengzhu Swinburne, Ian A. Megason, Sean G. ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes |
title |
ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes |
title_full |
ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes |
title_fullStr |
ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes |
title_full_unstemmed |
ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes |
title_short |
ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes |
title_sort | acme: automated cell morphology extractor for comprehensive reconstruction of cell membranes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3516542/ https://www.ncbi.nlm.nih.gov/pubmed/23236265 http://dx.doi.org/10.1371/journal.pcbi.1002780 |
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