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PLoS One. 2012;7(2):e30576. doi: 10.1371/journal.pone.0030576. Epub 2012 Feb 9.

Segmentation of multi-isotope imaging mass spectrometry data for semi-automatic detection of regions of interest.

Author information

1
Max Planck Institute of Psychiatry, Proteomics and Biomarkers, Munich, Germany.

Abstract

Multi-isotope imaging mass spectrometry (MIMS) associates secondary ion mass spectrometry (SIMS) with detection of several atomic masses, the use of stable isotopes as labels, and affiliated quantitative image-analysis software. By associating image and measure, MIMS allows one to obtain quantitative information about biological processes in sub-cellular domains. MIMS can be applied to a wide range of biomedical problems, in particular metabolism and cell fate [1], [2], [3]. In order to obtain morphologically pertinent data from MIMS images, we have to define regions of interest (ROIs). ROIs are drawn by hand, a tedious and time-consuming process. We have developed and successfully applied a support vector machine (SVM) for segmentation of MIMS images that allows fast, semi-automatic boundary detection of regions of interests. Using the SVM, high-quality ROIs (as compared to an expert's manual delineation) were obtained for 2 types of images derived from unrelated data sets. This automation simplifies, accelerates and improves the post-processing analysis of MIMS images. This approach has been integrated into "Open MIMS," an ImageJ-plugin for comprehensive analysis of MIMS images that is available online at http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php.

PMID:
22347386
PMCID:
PMC3276494
DOI:
10.1371/journal.pone.0030576
[Indexed for MEDLINE]
Free PMC Article

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