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Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks
Cellular calcium fluorescence imaging utilized to study cellular behaviors typically results in large datasets and a profound need for standardized and accurate analysis methods. Here, we describe open-source software (4SM) to overcome these limitations using an automated machine learning pipeline f...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674926/ https://www.ncbi.nlm.nih.gov/pubmed/36595928 http://dx.doi.org/10.1016/j.xpro.2022.101852 |
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author | Moghnieh, Hussein Kamran, Sharif Amit Hossain, Khondker Fariha Kuol, Nyanbol Riar, Sarah Bartlett, Allison Tavakkoli, Alireza Baker, Salah A. |
author_facet | Moghnieh, Hussein Kamran, Sharif Amit Hossain, Khondker Fariha Kuol, Nyanbol Riar, Sarah Bartlett, Allison Tavakkoli, Alireza Baker, Salah A. |
author_sort | Moghnieh, Hussein |
collection | PubMed |
description | Cellular calcium fluorescence imaging utilized to study cellular behaviors typically results in large datasets and a profound need for standardized and accurate analysis methods. Here, we describe open-source software (4SM) to overcome these limitations using an automated machine learning pipeline for subcellular calcium signal segmentation of spatiotemporal maps. The primary use of 4SM is to analyze spatiotemporal maps of calcium activities within cells or across multiple cells. For complete details on the use and execution of this protocol, please refer to Kamran et al. (2022).(1) |
format | Online Article Text |
id | pubmed-9674926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-96749262022-11-20 Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks Moghnieh, Hussein Kamran, Sharif Amit Hossain, Khondker Fariha Kuol, Nyanbol Riar, Sarah Bartlett, Allison Tavakkoli, Alireza Baker, Salah A. STAR Protoc Protocol Cellular calcium fluorescence imaging utilized to study cellular behaviors typically results in large datasets and a profound need for standardized and accurate analysis methods. Here, we describe open-source software (4SM) to overcome these limitations using an automated machine learning pipeline for subcellular calcium signal segmentation of spatiotemporal maps. The primary use of 4SM is to analyze spatiotemporal maps of calcium activities within cells or across multiple cells. For complete details on the use and execution of this protocol, please refer to Kamran et al. (2022).(1) Elsevier 2022-11-15 /pmc/articles/PMC9674926/ /pubmed/36595928 http://dx.doi.org/10.1016/j.xpro.2022.101852 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Moghnieh, Hussein Kamran, Sharif Amit Hossain, Khondker Fariha Kuol, Nyanbol Riar, Sarah Bartlett, Allison Tavakkoli, Alireza Baker, Salah A. Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks |
title | Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks |
title_full | Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks |
title_fullStr | Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks |
title_full_unstemmed | Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks |
title_short | Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks |
title_sort | software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674926/ https://www.ncbi.nlm.nih.gov/pubmed/36595928 http://dx.doi.org/10.1016/j.xpro.2022.101852 |
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