Open AccessCellSeT: Novel Software to Extract and Analyze Structured Networks of Plant Cells from Confocal Images
Author(s) -
Michael P. Pound,
Andrew P. French,
Darren M. Wells,
Malcolm J. Bennett,
Tony Pridmore
Publication year - 2012
Publication title -
the plant cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.324
H-Index - 341
eISSN - 1532-298X
pISSN - 1040-4651
DOI - 10.1105/tpc.112.096289
Subject(s) - confocal , computer science , software , bottleneck , confocal microscopy , process (computing) , scale (ratio) , microscope , artificial intelligence , biological system , computer vision , data mining , biology , microbiology and biotechnology , pathology , embedded system , physics , geometry , mathematics , quantum mechanics , programming language , operating system , medicine
It is increasingly important in life sciences that many cell-scale and tissue-scale measurements are quantified from confocal microscope images. However, extracting and analyzing large-scale confocal image data sets represents a major bottleneck for researchers. To aid this process, CellSeT software has been developed, which utilizes tissue-scale structure to help segment individual cells. We provide examples of how the CellSeT software can be used to quantify fluorescence of hormone-responsive nuclear reporters, determine membrane protein polarity, extract cell and tissue geometry for use in later modeling, and take many additional biologically relevant measures using an extensible plug-in toolset. Application of CellSeT promises to remove subjectivity from the resulting data sets and facilitate higher-throughput, quantitative approaches to plant cell research.
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