@article {4984,
	title = {Biological imaging software tools.},
	journal = {Nature Methods},
	volume = {9},
	year = {2012},
	month = {2012 Jun 28},
	pages = {697-710},
	abstract = {<p>Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.</p>
},
	issn = {1548-7105},
	doi = {10.1038/nmeth.2084},
	author = {Eliceiri, Kevin W and Berthold, Michael R and Goldberg, Ilya G and Ib{\'a}{\~n}ez, Luis and Manjunath, B S and Martone, Maryann E and Murphy, Robert F and Peng, Hanchuan and Plant, Anne L and Roysam, Badrinath and Stuurmann, Nico and Swedlow, Jason R and Tomancak, Pavel and Carpenter, Anne E.}
}
@article {4064,
	title = {Towards automated high-throughput screening of C. elegans on agar.},
	journal = {Arxiv},
	year = {2010},
	abstract = {<p>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.</p>
},
	author = {Kabra, Mayank and Conery, Annie L. and O{\textquoteright}Rourke, Eyleen J. and Xie, Xin and Ljosa. Vebjorn and Jones, Thouis R. and Ausubel, Frederick M. and Ruvkun, Gary and Carpenter, Anne E. and Freund, Yoav}
}
