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

Abstract

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.

Keywords

• ImageJ
• macro language
• segmentation
• <i>Tetrahymena</i>