Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ

Abstract Measuring the concentration and viability of fungal cells is an important and fundamental procedure in scientific research and industrial fermentation. In consideration of the drawbacks of manual cell counting, large quantities of fungal cells require methods that provide easy, objective an...

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Autores principales: Chenxi Li, Xiaoyu Ma, Jing Deng, Jiajia Li, Yanjie Liu, Xudong Zhu, Jin Liu, Ping Zhang
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Lenguaje:EN
Publicado: Wiley-VCH 2021
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Acceso en línea:https://doaj.org/article/f8535fa64b4743b195f5be0fed9ebaf6
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spelling oai:doaj.org-article:f8535fa64b4743b195f5be0fed9ebaf62021-11-09T05:07:05ZMachine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ1618-28631618-024010.1002/elsc.202100055https://doaj.org/article/f8535fa64b4743b195f5be0fed9ebaf62021-11-01T00:00:00Zhttps://doi.org/10.1002/elsc.202100055https://doaj.org/toc/1618-0240https://doaj.org/toc/1618-2863Abstract Measuring the concentration and viability of fungal cells is an important and fundamental procedure in scientific research and industrial fermentation. In consideration of the drawbacks of manual cell counting, large quantities of fungal cells require methods that provide easy, objective and reproducible high‐throughput calculations, especially for samples in complicated backgrounds. To answer this challenge, we explored and developed an easy‐to‐use fungal cell counting pipeline that combined the machine learning‐based ilastik tool with the freeware ImageJ, as well as a conventional photomicroscope. Briefly, learning from labels provided by the user, ilastik performs segmentation and classification automatically in batch processing mode and thus discriminates fungal cells from complex backgrounds. The files processed through ilastik can be recognized by ImageJ, which can compute the numeric results with the macro ‘Fungal Cell Counter’. Taking the yeast Cryptococccus deneoformans and the filamentous fungus Pestalotiopsis microspora as examples, we observed that the customizable software algorithm reduced inter‐operator errors significantly and achieved accurate and objective results, while manual counting with a haemocytometer exhibited some errors between repeats and required more time. In summary, a convenient, rapid, reproducible and extremely low‐cost method to count yeast cells and fungal spores is described here, which can be applied to multiple kinds of eucaryotic microorganisms in genetics, cell biology and industrial fermentation.Chenxi LiXiaoyu MaJing DengJiajia LiYanjie LiuXudong ZhuJin LiuPing ZhangWiley-VCHarticlebatch processingfungal sporesilastikImageJyeastBiotechnologyTP248.13-248.65ENEngineering in Life Sciences, Vol 21, Iss 11, Pp 769-777 (2021)
institution DOAJ
collection DOAJ
language EN
topic batch processing
fungal spores
ilastik
ImageJ
yeast
Biotechnology
TP248.13-248.65
spellingShingle batch processing
fungal spores
ilastik
ImageJ
yeast
Biotechnology
TP248.13-248.65
Chenxi Li
Xiaoyu Ma
Jing Deng
Jiajia Li
Yanjie Liu
Xudong Zhu
Jin Liu
Ping Zhang
Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ
description Abstract Measuring the concentration and viability of fungal cells is an important and fundamental procedure in scientific research and industrial fermentation. In consideration of the drawbacks of manual cell counting, large quantities of fungal cells require methods that provide easy, objective and reproducible high‐throughput calculations, especially for samples in complicated backgrounds. To answer this challenge, we explored and developed an easy‐to‐use fungal cell counting pipeline that combined the machine learning‐based ilastik tool with the freeware ImageJ, as well as a conventional photomicroscope. Briefly, learning from labels provided by the user, ilastik performs segmentation and classification automatically in batch processing mode and thus discriminates fungal cells from complex backgrounds. The files processed through ilastik can be recognized by ImageJ, which can compute the numeric results with the macro ‘Fungal Cell Counter’. Taking the yeast Cryptococccus deneoformans and the filamentous fungus Pestalotiopsis microspora as examples, we observed that the customizable software algorithm reduced inter‐operator errors significantly and achieved accurate and objective results, while manual counting with a haemocytometer exhibited some errors between repeats and required more time. In summary, a convenient, rapid, reproducible and extremely low‐cost method to count yeast cells and fungal spores is described here, which can be applied to multiple kinds of eucaryotic microorganisms in genetics, cell biology and industrial fermentation.
format article
author Chenxi Li
Xiaoyu Ma
Jing Deng
Jiajia Li
Yanjie Liu
Xudong Zhu
Jin Liu
Ping Zhang
author_facet Chenxi Li
Xiaoyu Ma
Jing Deng
Jiajia Li
Yanjie Liu
Xudong Zhu
Jin Liu
Ping Zhang
author_sort Chenxi Li
title Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ
title_short Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ
title_full Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ
title_fullStr Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ
title_full_unstemmed Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ
title_sort machine learning‐based automated fungal cell counting under a complicated background with ilastik and imagej
publisher Wiley-VCH
publishDate 2021
url https://doaj.org/article/f8535fa64b4743b195f5be0fed9ebaf6
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