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Background <p>The development of filamentous fungal cells from spore to mature mycelium is influenced by a myriad of environmental conditions. Traditional methods for quantifying various fungal cell morphologies in liquid media via microscopy are labor-intensive and prone to user-dependent variability.</p> Methods <p>This study addresses these challenges by introducing a streamlined workflow for fungal cell analysis, utilizing ilastik-based image segmentation and Python for fast data preparation and analysis. The goal is to offer a user-friendly method for counting and analyzing different fungal cell morphologies and their sizes that is free of charge and accessible to users with minimal programming experience.</p> Results <p>The presented workflow significantly reduces the time required to determine cell developmental stages from microscopy images. It enables rapid results, as compared to manual counting. It also minimizes inter-user variability, enhancing consistency across analyses. It provides a high-throughput, automated solution for fungal cell analysis allowing users to process hundreds of images (n&#xa0;= 48-438) for analysis in a short time period (9-56 min).</p> Conclusion <p>This approach shows great potential for accelerating research and improving data reliability in biological studies. Furthermore, it provides laboratories in resource limited settings with a free to use solution.</p>

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An automated workflow for fungal cell counting, cell size and data analysis: enhancing throughput and accuracy with the cell analysis and counting tool using ilastik software (caactus)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

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  • Jakob Scheler,
  • Dominik Kutra,
  • Vincent Beliveau,
  • Johannes Seiler,
  • Samuel Pröll,
  • Hannah Sittel,
  • Alina Nowak-Rainer,
  • Hannah Holzer,
  • Cornelia Lass-Flörl,
  • Ulrike Binder

摘要

Background

The development of filamentous fungal cells from spore to mature mycelium is influenced by a myriad of environmental conditions. Traditional methods for quantifying various fungal cell morphologies in liquid media via microscopy are labor-intensive and prone to user-dependent variability.

Methods

This study addresses these challenges by introducing a streamlined workflow for fungal cell analysis, utilizing ilastik-based image segmentation and Python for fast data preparation and analysis. The goal is to offer a user-friendly method for counting and analyzing different fungal cell morphologies and their sizes that is free of charge and accessible to users with minimal programming experience.

Results

The presented workflow significantly reduces the time required to determine cell developmental stages from microscopy images. It enables rapid results, as compared to manual counting. It also minimizes inter-user variability, enhancing consistency across analyses. It provides a high-throughput, automated solution for fungal cell analysis allowing users to process hundreds of images (n = 48-438) for analysis in a short time period (9-56 min).

Conclusion

This approach shows great potential for accelerating research and improving data reliability in biological studies. Furthermore, it provides laboratories in resource limited settings with a free to use solution.