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A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images

BACKGROUND: Recently a series of algorithms have been developed, providing automatic tools for tracing C. elegans embryonic cell lineage. In these algorithms, 3D images collected from a confocal laser scanning microscope were processed, the output of which is cell lineage with cell division history...

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Detalles Bibliográficos
Autores principales: Chen, Long, Chan, Leanne Lai Hang, Zhao, Zhongying, Yan, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3903074/
https://www.ncbi.nlm.nih.gov/pubmed/24252066
http://dx.doi.org/10.1186/1471-2105-14-328
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author Chen, Long
Chan, Leanne Lai Hang
Zhao, Zhongying
Yan, Hong
author_facet Chen, Long
Chan, Leanne Lai Hang
Zhao, Zhongying
Yan, Hong
author_sort Chen, Long
collection PubMed
description BACKGROUND: Recently a series of algorithms have been developed, providing automatic tools for tracing C. elegans embryonic cell lineage. In these algorithms, 3D images collected from a confocal laser scanning microscope were processed, the output of which is cell lineage with cell division history and cell positions with time. However, current image segmentation algorithms suffer from high error rate especially after 350-cell stage because of low signal-noise ratio as well as low resolution along the Z axis (0.5-1 microns). As a result, correction of the errors becomes a huge burden. These errors are mainly produced in the segmentation of nuclei. Thus development of a more accurate image segmentation algorithm will alleviate the hurdle for automated analysis of cell lineage. RESULTS: This paper presents a new type of nuclei segmentation method embracing an bi-directional prediction procedure, which can greatly reduce the number of false negative errors, the most common errors in the previous segmentation. In this method, we first use a 2D region growing technique together with the level-set method to generate accurate 2D slices. Then a modified gradient method instead of the existing 3D local maximum method is adopted to detect all the 2D slices located in the nuclei center, each of which corresponds to one nucleus. Finally, the bi-directional pred- iction method based on the images before and after the current time point is introduced into the system to predict the nuclei in low quality parts of the images. The result of our method shows a notable improvement in the accuracy rate. For each nucleus, its precise location, volume and gene expression value (gray value) is also obtained, all of which will be useful in further downstream analyses. CONCLUSIONS: The result of this research demonstrates the advantages of the bi-directional prediction method in the nuclei segmentation over that of StarryNite/MatLab StarryNite. Several other modifications adopted in our nuclei segmentation system are also discussed.
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spelling pubmed-39030742014-02-11 A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images Chen, Long Chan, Leanne Lai Hang Zhao, Zhongying Yan, Hong BMC Bioinformatics Methodology Article BACKGROUND: Recently a series of algorithms have been developed, providing automatic tools for tracing C. elegans embryonic cell lineage. In these algorithms, 3D images collected from a confocal laser scanning microscope were processed, the output of which is cell lineage with cell division history and cell positions with time. However, current image segmentation algorithms suffer from high error rate especially after 350-cell stage because of low signal-noise ratio as well as low resolution along the Z axis (0.5-1 microns). As a result, correction of the errors becomes a huge burden. These errors are mainly produced in the segmentation of nuclei. Thus development of a more accurate image segmentation algorithm will alleviate the hurdle for automated analysis of cell lineage. RESULTS: This paper presents a new type of nuclei segmentation method embracing an bi-directional prediction procedure, which can greatly reduce the number of false negative errors, the most common errors in the previous segmentation. In this method, we first use a 2D region growing technique together with the level-set method to generate accurate 2D slices. Then a modified gradient method instead of the existing 3D local maximum method is adopted to detect all the 2D slices located in the nuclei center, each of which corresponds to one nucleus. Finally, the bi-directional pred- iction method based on the images before and after the current time point is introduced into the system to predict the nuclei in low quality parts of the images. The result of our method shows a notable improvement in the accuracy rate. For each nucleus, its precise location, volume and gene expression value (gray value) is also obtained, all of which will be useful in further downstream analyses. CONCLUSIONS: The result of this research demonstrates the advantages of the bi-directional prediction method in the nuclei segmentation over that of StarryNite/MatLab StarryNite. Several other modifications adopted in our nuclei segmentation system are also discussed. BioMed Central 2013-11-19 /pmc/articles/PMC3903074/ /pubmed/24252066 http://dx.doi.org/10.1186/1471-2105-14-328 Text en Copyright © 2013 Chen et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Chen, Long
Chan, Leanne Lai Hang
Zhao, Zhongying
Yan, Hong
A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images
title A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images
title_full A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images
title_fullStr A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images
title_full_unstemmed A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images
title_short A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images
title_sort novel cell nuclei segmentation method for 3d c. elegans embryonic time-lapse images
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3903074/
https://www.ncbi.nlm.nih.gov/pubmed/24252066
http://dx.doi.org/10.1186/1471-2105-14-328
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