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Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension

Previous methods to measure protozoan numbers mostly rely on manual counting, which suffers from high variation and poor efficiency. Although advanced counting devices are available, the specialized and usually expensive machinery precludes their prevalent utilization in the regular laboratory routi...

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Autores principales: Kurnia, Kevin Adi, Sampurna, Bonifasius Putera, Audira, Gilbert, Juniardi, Stevhen, Vasquez, Ross D., Roldan, Marri Jmelou M., Tsao, Che-Chia, Hsiao, Chung-Der
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9181243/
https://www.ncbi.nlm.nih.gov/pubmed/35682689
http://dx.doi.org/10.3390/ijms23116009
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author Kurnia, Kevin Adi
Sampurna, Bonifasius Putera
Audira, Gilbert
Juniardi, Stevhen
Vasquez, Ross D.
Roldan, Marri Jmelou M.
Tsao, Che-Chia
Hsiao, Chung-Der
author_facet Kurnia, Kevin Adi
Sampurna, Bonifasius Putera
Audira, Gilbert
Juniardi, Stevhen
Vasquez, Ross D.
Roldan, Marri Jmelou M.
Tsao, Che-Chia
Hsiao, Chung-Der
author_sort Kurnia, Kevin Adi
collection PubMed
description Previous methods to measure protozoan numbers mostly rely on manual counting, which suffers from high variation and poor efficiency. Although advanced counting devices are available, the specialized and usually expensive machinery precludes their prevalent utilization in the regular laboratory routine. In this study, we established the ImageJ-based workflow to quantify ciliate numbers in a high-throughput manner. We conducted Tetrahymena number measurement using five different methods: particle analyzer method (PAM), find maxima method (FMM), trainable WEKA segmentation method (TWS), watershed segmentation method (WSM) and StarDist method (SDM), and compared their results with the data obtained from the manual counting. Among the five methods tested, all of them could yield decent results, but the deep-learning-based SDM displayed the best performance for Tetrahymena cell counting. The optimized methods reported in this paper provide scientists with a convenient tool to perform cell counting for Tetrahymena ecotoxicity assessment.
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spelling pubmed-91812432022-06-10 Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension Kurnia, Kevin Adi Sampurna, Bonifasius Putera Audira, Gilbert Juniardi, Stevhen Vasquez, Ross D. Roldan, Marri Jmelou M. Tsao, Che-Chia Hsiao, Chung-Der Int J Mol Sci Article Previous methods to measure protozoan numbers mostly rely on manual counting, which suffers from high variation and poor efficiency. Although advanced counting devices are available, the specialized and usually expensive machinery precludes their prevalent utilization in the regular laboratory routine. In this study, we established the ImageJ-based workflow to quantify ciliate numbers in a high-throughput manner. We conducted Tetrahymena number measurement using five different methods: particle analyzer method (PAM), find maxima method (FMM), trainable WEKA segmentation method (TWS), watershed segmentation method (WSM) and StarDist method (SDM), and compared their results with the data obtained from the manual counting. Among the five methods tested, all of them could yield decent results, but the deep-learning-based SDM displayed the best performance for Tetrahymena cell counting. The optimized methods reported in this paper provide scientists with a convenient tool to perform cell counting for Tetrahymena ecotoxicity assessment. MDPI 2022-05-26 /pmc/articles/PMC9181243/ /pubmed/35682689 http://dx.doi.org/10.3390/ijms23116009 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kurnia, Kevin Adi
Sampurna, Bonifasius Putera
Audira, Gilbert
Juniardi, Stevhen
Vasquez, Ross D.
Roldan, Marri Jmelou M.
Tsao, Che-Chia
Hsiao, Chung-Der
Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension
title Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension
title_full Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension
title_fullStr Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension
title_full_unstemmed Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension
title_short Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension
title_sort performance comparison of five methods for tetrahymena number counting on the imagej platform: assessing the built-in tool and machine-learning-based extension
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9181243/
https://www.ncbi.nlm.nih.gov/pubmed/35682689
http://dx.doi.org/10.3390/ijms23116009
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