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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2021
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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) |
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batch processing fungal spores ilastik ImageJ yeast Biotechnology TP248.13-248.65 |
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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 |
work_keys_str_mv |
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1718441350444089344 |