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High-throughput screening (HTS) using model organisms is a promising methodto identify a small number of genes or drugs potentially relevant to humanbiology or disease. In HTS experiments, robots and computers do a significantportion of the experimental work. However, one remaining major bottleneck isthe manual analysis of experimental results, which is commonly in the form ofmicroscopy images. This manual inspection is labor intensive, slow andsubjective. Here we report our progress towards applying computer vision andmachine learning methods to analyze HTS experiments that use Caenorhabditiselegans (C. elegans) worms grown on agar. Our main contribution is a robustsegmentation algorithm for separating the worms from the background usingbrightfield images. We also show that by combining the output of thissegmentation algorithm with an algorithm to detect the fluorescent dye, NileRed, we can reliably distinguish different fluorescence-based phenotypes eventhough the visual differences are subtle. The accuracy of our method is similarto that of expert human analysts. This new capability is a significant steptowards fully automated HTS experiments using C. elegans.

Towards automated high-throughput screening of C. elegans on agar