A fully automated deep learning pipeline for high-throughput colony segmentation and classification

Biol Open. 2020 Jun 23;9(6):bio052936. doi: 10.1242/bio.052936.


Adenine auxotrophy is a commonly used non-selective genetic marker in yeast research. It allows investigators to easily visualize and quantify various genetic and epigenetic events by simply reading out colony color. However, manual counting of large numbers of colonies is extremely time-consuming, difficult to reproduce and possibly inaccurate. Using cutting-edge neural networks, we have developed a fully automated pipeline for colony segmentation and classification, which speeds up white/red colony quantification 100-fold over manual counting by an experienced researcher. Our approach uses readily available training data and can be smoothly integrated into existing protocols, vastly speeding up screening assays and increasing the statistical power of experiments that employ adenine auxotrophy.

Keywords: Adenine auxotrophy; Deep learning; Growth assay; Neural networks; Yeast.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Colony Count, Microbial / methods*
  • Colony Count, Microbial / standards
  • Deep Learning*
  • High-Throughput Screening Assays*
  • Image Processing, Computer-Assisted
  • Neural Networks, Computer
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Yeasts