Title:
Training and Validation Data for Automated Head versus Tail Classification and Cell Identification in C. elegans
Training and Validation Data for Automated Head versus Tail Classification and Cell Identification in C. elegans
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Author(s)
Zhan, Mei
Crane, Matthew Muria
Lu, Hang
Ch'ng, QueeLim
Entchev, Eugeni
Caballero, Antonio
de Abreu, Diana Andrea Fernandes
Crane, Matthew Muria
Lu, Hang
Ch'ng, QueeLim
Entchev, Eugeni
Caballero, Antonio
de Abreu, Diana Andrea Fernandes
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Abstract
We demonstrate the utility of a generalizable classification routine for biological image processing by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. These data files contain all of the training and validation data used for the construction and validation of these classifiers.
Sponsor
National Institutes of Health (U.S.)
Wellcome Trust (London, England)
Biotechnology and Biological Sciences Research Council (Great Britain)
European Research Council
National Science Foundation (U.S.)
Wellcome Trust (London, England)
Biotechnology and Biological Sciences Research Council (Great Britain)
European Research Council
National Science Foundation (U.S.)
Date Issued
2015
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Dataset