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Sci Rep. 2015 Jan 15;5:7794. doi: 10.1038/srep07794.

Semi-automatic organelle detection on transmission electron microscopic images.

Author information

1
Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa 277-8562, Japan.
2
1] Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa 277-8562, Japan [2] Research and Development Division, LPixel Inc., Bunkyo-ku, Tokyo 150-0002, Japan.
3
RIKEN Center for Sustainable Resource Science, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan.
4
Faculty of Science, Japan Women's University, Bunkyo-ku, Tokyo 112-8681, Japan.

Abstract

Recent advances in the acquisition of large-scale datasets of transmission electron microscope images have allowed researchers to determine the number and the distribution of subcellular ultrastructures at both the cellular level and the tissue level. For this purpose, it would be very useful to have a computer-assisted system to detect the structures of interest, such as organelles. Using our original image recognition framework CARTA (Clustering-Aided Rapid Training Agent), combined with procedures to highlight and enlarge regions of interest on the image, we have developed a successful method for the semi-automatic detection of plant organelles including mitochondria, amyloplasts, chloroplasts, etioplasts, and Golgi stacks in transmission electron microscope images. Our proposed semi-automatic detection system will be helpful for labelling organelles in the interpretation and/or quantitative analysis of large-scale electron microscope imaging data.

PMID:
25589024
PMCID:
PMC4295107
DOI:
10.1038/srep07794
[Indexed for MEDLINE]
Free PMC Article

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