Name
Abstract | ||
|---|---|---|
High-throughput screening (HTS) using model organisms is a promising method
to identify a small number of genes or drugs potentially relevant to human
biology or disease. In HTS experiments, robots and computers do a significant
portion of the experimental work. However, one remaining major bottleneck is
the manual analysis of experimental results, which is commonly in the form of
microscopy images. This manual inspection is labor intensive, slow and
subjective. Here we report our progress towards applying computer vision and
machine learning methods to analyze HTS experiments that use Caenorhabditis
elegans (C. elegans) worms grown on agar. Our main contribution is a robust
segmentation algorithm for separating the worms from the background using
brightfield images. We also show that by combining the output of this
segmentation algorithm with an algorithm to detect the fluorescent dye, Nile
Red, we can reliably distinguish different fluorescence-based phenotypes even
though the visual differences are subtle. The accuracy of our method is similar
to that of expert human analysts. This new capability is a significant step
towards fully automated HTS experiments using C. elegans. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.7490/f1000research.252.1 | Clinical Orthopaedics and Related Research |
Keywords | DocType | Volume |
biology,genetics,microbiology,cell biology,medicine,neuroscience,oncology,anaesthesiology,physiology,ecology,publishing,health,plant biology | Journal | abs/1003.4 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mayank Kabra | 1 | 60 | 3.59 |
| Annie L. Conery | 2 | 9 | 1.43 |
| Eyleen J. O'Rourke | 3 | 0 | 0.34 |
| Xin Xie | 4 | 2 | 4.21 |
| Vebjorn Ljosa | 5 | 208 | 12.43 |
| Thouis R. Jones | 6 | 1193 | 73.62 |
| Frederick M. Ausubel | 7 | 9 | 1.77 |
| Gary Ruvkun | 8 | 0 | 0.34 |
| Anne E. Carpenter | 9 | 137 | 12.90 |
| Yoav Freund | 10 | 13261 | 1773.95 |