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 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 back

SUBMITTER: Li C 

PROVIDER: S-EPMC8576076 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature