Cargando…
Graphical-model framework for automated annotation of cell identities in dense cellular images
Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers’ experiences. Here, we present a probabilistic-graphical-model framework, CR...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032398/ https://www.ncbi.nlm.nih.gov/pubmed/33625357 http://dx.doi.org/10.7554/eLife.60321 |
_version_ | 1783676211238535168 |
---|---|
author | Chaudhary, Shivesh Lee, Sol Ah Li, Yueyi Patel, Dhaval S Lu, Hang |
author_facet | Chaudhary, Shivesh Lee, Sol Ah Li, Yueyi Patel, Dhaval S Lu, Hang |
author_sort | Chaudhary, Shivesh |
collection | PubMed |
description | Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers’ experiences. Here, we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in Caenorhabditis elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems. |
format | Online Article Text |
id | pubmed-8032398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-80323982021-04-12 Graphical-model framework for automated annotation of cell identities in dense cellular images Chaudhary, Shivesh Lee, Sol Ah Li, Yueyi Patel, Dhaval S Lu, Hang eLife Computational and Systems Biology Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers’ experiences. Here, we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in Caenorhabditis elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems. eLife Sciences Publications, Ltd 2021-02-24 /pmc/articles/PMC8032398/ /pubmed/33625357 http://dx.doi.org/10.7554/eLife.60321 Text en © 2021, Chaudhary et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Chaudhary, Shivesh Lee, Sol Ah Li, Yueyi Patel, Dhaval S Lu, Hang Graphical-model framework for automated annotation of cell identities in dense cellular images |
title | Graphical-model framework for automated annotation of cell identities in dense cellular images |
title_full | Graphical-model framework for automated annotation of cell identities in dense cellular images |
title_fullStr | Graphical-model framework for automated annotation of cell identities in dense cellular images |
title_full_unstemmed | Graphical-model framework for automated annotation of cell identities in dense cellular images |
title_short | Graphical-model framework for automated annotation of cell identities in dense cellular images |
title_sort | graphical-model framework for automated annotation of cell identities in dense cellular images |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032398/ https://www.ncbi.nlm.nih.gov/pubmed/33625357 http://dx.doi.org/10.7554/eLife.60321 |
work_keys_str_mv | AT chaudharyshivesh graphicalmodelframeworkforautomatedannotationofcellidentitiesindensecellularimages AT leesolah graphicalmodelframeworkforautomatedannotationofcellidentitiesindensecellularimages AT liyueyi graphicalmodelframeworkforautomatedannotationofcellidentitiesindensecellularimages AT pateldhavals graphicalmodelframeworkforautomatedannotationofcellidentitiesindensecellularimages AT luhang graphicalmodelframeworkforautomatedannotationofcellidentitiesindensecellularimages |