Segmentation of multi-isotope imaging mass spectrometry data for semi-automatic detection of regions of interest
| Autor: | Christoph W. Turck, Philipp Gormanns, Stefan Reckow, J. Collin Poczatek, Claude Lechene |
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| Rok vydání: | 2011 |
| Předmět: |
Diagnostic Imaging
Support Vector Machine Computer science lcsh:Medicine 02 engineering and technology Mass spectrometry Bioinformatics Biochemistry Mass spectrometry imaging Mass Spectrometry 03 medical and health sciences Neuroimaging Isotopes 0202 electrical engineering electronic engineering information engineering Medical imaging Methods Segmentation lcsh:Science Biology 030304 developmental biology 0303 health sciences Multidisciplinary Stable isotope ratio business.industry Systems Biology lcsh:R Pattern recognition Computing Methods Support vector machine Secondary ion mass spectrometry Metabolism Computer Science 020201 artificial intelligence & image processing lcsh:Q Artificial intelligence Semi automatic business Software Algorithms Research Article |
| Zdroj: | PLoS ONE PLoS ONE, Vol 7, Iss 2, p e30576 (2012) |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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