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Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification

Identification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provid...

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Autores principales: Wu, Tzu-Ching, Wang, Xu, Li, Linlin, Bu, Ye, Umulis, David M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110989/
https://www.ncbi.nlm.nih.gov/pubmed/33972575
http://dx.doi.org/10.1038/s41598-021-88966-2
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author Wu, Tzu-Ching
Wang, Xu
Li, Linlin
Bu, Ye
Umulis, David M.
author_facet Wu, Tzu-Ching
Wang, Xu
Li, Linlin
Bu, Ye
Umulis, David M.
author_sort Wu, Tzu-Ching
collection PubMed
description Identification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio. The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets.
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spelling pubmed-81109892021-05-12 Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification Wu, Tzu-Ching Wang, Xu Li, Linlin Bu, Ye Umulis, David M. Sci Rep Article Identification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio. The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets. Nature Publishing Group UK 2021-05-10 /pmc/articles/PMC8110989/ /pubmed/33972575 http://dx.doi.org/10.1038/s41598-021-88966-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wu, Tzu-Ching
Wang, Xu
Li, Linlin
Bu, Ye
Umulis, David M.
Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification
title Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification
title_full Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification
title_fullStr Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification
title_full_unstemmed Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification
title_short Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification
title_sort automatic wavelet-based 3d nuclei segmentation and analysis for multicellular embryo quantification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110989/
https://www.ncbi.nlm.nih.gov/pubmed/33972575
http://dx.doi.org/10.1038/s41598-021-88966-2
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