Robust colony recognition for high-throughput growth analysis from suboptimal low-magnification brightfield micrographs

New technological advances have enabled high-throughput phenotyping at the single-cell level. However, analyzing the large amount of data automatically and accurately is a great challenge. Currently available software achieves cell and colony tracking through the use of either manual curation of images, which is time consuming, or high-resolution images requiring specialized microscopy setups or fluorescence, which limits applicability and results in greatly decreased experimental throughput. Here we introduce a new algorithm, Processing Images Easily (PIE), that automatically tracks colonies of the yeast Saccharomyces cerevisiae in low-magnification brightfield images by combining adaptive object-center detection with gradient-based object-outline detection. We tested the performance of PIE on low-magnification brightfield time-lapse images. PIE recognizes colony outlines very robustly and accurately across a wide range of image brightnesses and focal depths. We show that PIE allows for unbiased and precise measurement of growth rates in a large number (>90,000) of microcolonies in a single time-lapse experiment.