Automated cell annotation in multi-cell images using an improved CRF_ID algorithm
Cell identification is an important yet difficult process in data analysis of biological images. Previously, we developed an automated cell identification method called CRF_ID and demonstrated its high performance in Caenorhabditis elegans whole-brain images (Chaudhary et al., 2021). However, becaus...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2025-01-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/89050 |
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| author | Hyun Jee Lee Jingting Liang Shivesh Chaudhary Sihoon Moon Zikai Yu Taihong Wu He Liu Myung-Kyu Choi Yun Zhang Hang Lu |
| author_facet | Hyun Jee Lee Jingting Liang Shivesh Chaudhary Sihoon Moon Zikai Yu Taihong Wu He Liu Myung-Kyu Choi Yun Zhang Hang Lu |
| author_sort | Hyun Jee Lee |
| collection | DOAJ |
| description | Cell identification is an important yet difficult process in data analysis of biological images. Previously, we developed an automated cell identification method called CRF_ID and demonstrated its high performance in Caenorhabditis elegans whole-brain images (Chaudhary et al., 2021). However, because the method was optimized for whole-brain imaging, comparable performance could not be guaranteed for application in commonly used C. elegans multi-cell images that display a subpopulation of cells. Here, we present an advancement, CRF_ID 2.0, that expands the generalizability of the method to multi-cell imaging beyond whole-brain imaging. To illustrate the application of the advance, we show the characterization of CRF_ID 2.0 in multi-cell imaging and cell-specific gene expression analysis in C. elegans. This work demonstrates that high-accuracy automated cell annotation in multi-cell imaging can expedite cell identification and reduce its subjectivity in C. elegans and potentially other biological images of various origins. |
| format | Article |
| id | doaj-art-ab69daf7cfa7409587bcbfbdfddd11c3 |
| institution | Kabale University |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-ab69daf7cfa7409587bcbfbdfddd11c32025-01-24T13:21:17ZengeLife Sciences Publications LtdeLife2050-084X2025-01-011210.7554/eLife.89050Automated cell annotation in multi-cell images using an improved CRF_ID algorithmHyun Jee Lee0https://orcid.org/0000-0001-9662-2063Jingting Liang1https://orcid.org/0009-0004-4284-257XShivesh Chaudhary2https://orcid.org/0000-0002-1928-0933Sihoon Moon3https://orcid.org/0000-0003-4540-9443Zikai Yu4https://orcid.org/0009-0004-1851-3493Taihong Wu5https://orcid.org/0000-0002-9760-6978He Liu6https://orcid.org/0000-0001-9418-9171Myung-Kyu Choi7Yun Zhang8https://orcid.org/0000-0002-7631-858XHang Lu9https://orcid.org/0000-0002-6881-660XSchool of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United StatesDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, United StatesSchool of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United StatesSchool of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United StatesInterdisciplinary BioEngineering Program, Georgia Institute of Technology, Atlanta, United StatesDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, United StatesDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, United StatesDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, United StatesDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United StatesSchool of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United States; Interdisciplinary BioEngineering Program, Georgia Institute of Technology, Atlanta, United StatesCell identification is an important yet difficult process in data analysis of biological images. Previously, we developed an automated cell identification method called CRF_ID and demonstrated its high performance in Caenorhabditis elegans whole-brain images (Chaudhary et al., 2021). However, because the method was optimized for whole-brain imaging, comparable performance could not be guaranteed for application in commonly used C. elegans multi-cell images that display a subpopulation of cells. Here, we present an advancement, CRF_ID 2.0, that expands the generalizability of the method to multi-cell imaging beyond whole-brain imaging. To illustrate the application of the advance, we show the characterization of CRF_ID 2.0 in multi-cell imaging and cell-specific gene expression analysis in C. elegans. This work demonstrates that high-accuracy automated cell annotation in multi-cell imaging can expedite cell identification and reduce its subjectivity in C. elegans and potentially other biological images of various origins.https://elifesciences.org/articles/89050cell identificationneural gene expressionimaging |
| spellingShingle | Hyun Jee Lee Jingting Liang Shivesh Chaudhary Sihoon Moon Zikai Yu Taihong Wu He Liu Myung-Kyu Choi Yun Zhang Hang Lu Automated cell annotation in multi-cell images using an improved CRF_ID algorithm eLife cell identification neural gene expression imaging |
| title | Automated cell annotation in multi-cell images using an improved CRF_ID algorithm |
| title_full | Automated cell annotation in multi-cell images using an improved CRF_ID algorithm |
| title_fullStr | Automated cell annotation in multi-cell images using an improved CRF_ID algorithm |
| title_full_unstemmed | Automated cell annotation in multi-cell images using an improved CRF_ID algorithm |
| title_short | Automated cell annotation in multi-cell images using an improved CRF_ID algorithm |
| title_sort | automated cell annotation in multi cell images using an improved crf id algorithm |
| topic | cell identification neural gene expression imaging |
| url | https://elifesciences.org/articles/89050 |
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