Training and Validation Data for Automated Head versus Tail Classification and Cell Identification in C. elegans

Zhan, Mei; Crane, Matthew Muria; Lu, Hang; Ch'ng, QueeLim; Entchev, Eugeni; Caballero, Antonio; de Abreu, Diana Andrea Fernandes

Title:

Training and Validation Data for Automated Head versus Tail Classification and Cell Identification in C. elegans

dc.contributor.author	Zhan, Mei
dc.contributor.author	Crane, Matthew Muria
dc.contributor.author	Lu, Hang
dc.contributor.author	Ch'ng, QueeLim
dc.contributor.author	Entchev, Eugeni
dc.contributor.author	Caballero, Antonio
dc.contributor.author	de Abreu, Diana Andrea Fernandes
dc.contributor.corporatename	Georgia Institute of Technology. School of Chemical and Biomolecular Engineering
dc.contributor.corporatename	Georgia Institute of Technology. Interdisciplinary Bioengineering Graduate Program
dc.contributor.corporatename	Kings College London. Institute of Psychiatry, Psychology, & Neuroscience. MRC Center for Developmental Neurobiology
dc.date.accessioned	2015-01-27T13:44:25Z
dc.date.available	2016-01-28T06:30:07Z
dc.date.issued	2015
dc.description	The data is comprised of three zip files containing both brightfield and fluorescent images of C. elegans and manual annotation data identifying the pharynx and individual cells within the worm. HeadvTailTrainingData.zip contains all the brightfield images and annotated binary images used for training of a head versus tail classifier. HeadvTailTestingData.zip contains all the brightfield images used to validate the head versus tail classifier. CellIDTrainingandTestingData.zip contains fluorescent images and annotated binary images for the training and validation of fluorescent cell identification classifiers in C. elegans.	en_US
dc.description	This dataset is licensed under a Creative Commons Attribution 4.0 International License. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.	en_US
dc.description.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.	en_US
dc.description.sponsorship	National Institutes of Health (U.S.)
dc.description.sponsorship	Wellcome Trust (London, England)
dc.description.sponsorship	Biotechnology and Biological Sciences Research Council (Great Britain)
dc.description.sponsorship	European Research Council
dc.description.sponsorship	National Science Foundation (U.S.)
dc.embargo.terms	1 year	en_US
dc.identifier.uri	http://hdl.handle.net/1853/53154
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.subject	Caenorhabditis elegans	en_US
dc.subject	Biological image processing	en_US
dc.subject	Computer vision	en_US
dc.subject	Recognition	en_US
dc.subject	Classification	en_US
dc.title	Training and Validation Data for Automated Head versus Tail Classification and Cell Identification in C. elegans	en_US
dc.type	Dataset	en_US
dspace.entity.type	Publication
local.contributor.author	Lu, Hang
local.contributor.corporatename	School of Chemical and Biomolecular Engineering
local.contributor.corporatename	College of Engineering
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