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Automated cellular annotation for high-resolution images of adult Caenorhabditis elegans
Motivation: Advances in high-resolution microscopy have recently made possible the analysis of gene expression at the level of individual cells. The fixed lineage of cells in the adult worm Caenorhabditis elegans makes this organism an ideal model for studying complex biological processes like devel...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694659/ https://www.ncbi.nlm.nih.gov/pubmed/23812982 http://dx.doi.org/10.1093/bioinformatics/btt223 |
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author | Aerni, Sarah J. Liu, Xiao Do, Chuong B. Gross, Samuel S. Nguyen, Andy Guo, Stephen D. Long, Fuhui Peng, Hanchuan Kim, Stuart S. Batzoglou, Serafim |
author_facet | Aerni, Sarah J. Liu, Xiao Do, Chuong B. Gross, Samuel S. Nguyen, Andy Guo, Stephen D. Long, Fuhui Peng, Hanchuan Kim, Stuart S. Batzoglou, Serafim |
author_sort | Aerni, Sarah J. |
collection | PubMed |
description | Motivation: Advances in high-resolution microscopy have recently made possible the analysis of gene expression at the level of individual cells. The fixed lineage of cells in the adult worm Caenorhabditis elegans makes this organism an ideal model for studying complex biological processes like development and aging. However, annotating individual cells in images of adult C.elegans typically requires expertise and significant manual effort. Automation of this task is therefore critical to enabling high-resolution studies of a large number of genes. Results: In this article, we describe an automated method for annotating a subset of 154 cells (including various muscle, intestinal and hypodermal cells) in high-resolution images of adult C.elegans. We formulate the task of labeling cells within an image as a combinatorial optimization problem, where the goal is to minimize a scoring function that compares cells in a test input image with cells from a training atlas of manually annotated worms according to various spatial and morphological characteristics. We propose an approach for solving this problem based on reduction to minimum-cost maximum-flow and apply a cross-entropy–based learning algorithm to tune the weights of our scoring function. We achieve 84% median accuracy across a set of 154 cell labels in this highly variable system. These results demonstrate the feasibility of the automatic annotation of microscopy-based images in adult C.elegans. Contact: saerni@cs.stanford.edu |
format | Online Article Text |
id | pubmed-3694659 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-36946592013-06-27 Automated cellular annotation for high-resolution images of adult Caenorhabditis elegans Aerni, Sarah J. Liu, Xiao Do, Chuong B. Gross, Samuel S. Nguyen, Andy Guo, Stephen D. Long, Fuhui Peng, Hanchuan Kim, Stuart S. Batzoglou, Serafim Bioinformatics Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany Motivation: Advances in high-resolution microscopy have recently made possible the analysis of gene expression at the level of individual cells. The fixed lineage of cells in the adult worm Caenorhabditis elegans makes this organism an ideal model for studying complex biological processes like development and aging. However, annotating individual cells in images of adult C.elegans typically requires expertise and significant manual effort. Automation of this task is therefore critical to enabling high-resolution studies of a large number of genes. Results: In this article, we describe an automated method for annotating a subset of 154 cells (including various muscle, intestinal and hypodermal cells) in high-resolution images of adult C.elegans. We formulate the task of labeling cells within an image as a combinatorial optimization problem, where the goal is to minimize a scoring function that compares cells in a test input image with cells from a training atlas of manually annotated worms according to various spatial and morphological characteristics. We propose an approach for solving this problem based on reduction to minimum-cost maximum-flow and apply a cross-entropy–based learning algorithm to tune the weights of our scoring function. We achieve 84% median accuracy across a set of 154 cell labels in this highly variable system. These results demonstrate the feasibility of the automatic annotation of microscopy-based images in adult C.elegans. Contact: saerni@cs.stanford.edu Oxford University Press 2013-07-01 2013-06-19 /pmc/articles/PMC3694659/ /pubmed/23812982 http://dx.doi.org/10.1093/bioinformatics/btt223 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany Aerni, Sarah J. Liu, Xiao Do, Chuong B. Gross, Samuel S. Nguyen, Andy Guo, Stephen D. Long, Fuhui Peng, Hanchuan Kim, Stuart S. Batzoglou, Serafim Automated cellular annotation for high-resolution images of adult Caenorhabditis elegans |
title | Automated cellular annotation for high-resolution images of adult Caenorhabditis elegans |
title_full | Automated cellular annotation for high-resolution images of adult Caenorhabditis elegans |
title_fullStr | Automated cellular annotation for high-resolution images of adult Caenorhabditis elegans |
title_full_unstemmed | Automated cellular annotation for high-resolution images of adult Caenorhabditis elegans |
title_short | Automated cellular annotation for high-resolution images of adult Caenorhabditis elegans |
title_sort | automated cellular annotation for high-resolution images of adult caenorhabditis elegans |
topic | Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694659/ https://www.ncbi.nlm.nih.gov/pubmed/23812982 http://dx.doi.org/10.1093/bioinformatics/btt223 |
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