Max Planck Institute of Psychiatry, Proteomics and Biomarkers, Munich, Germany.
PLoS One. 2012;7(2):e30576. doi: 10.1371/journal.pone.0030576. Epub 2012 Feb 9.
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.
多同位素成像质谱（MIMS）将二次离子质谱（SIMS）与检测多种原子质量、使用稳定同位素作为标记物以及相关的定量图像分析软件相结合。通过将图像和测量相结合，MIMS 可以让我们获得关于亚细胞区域中生物过程的定量信息。MIMS 可应用于广泛的生物医学问题，特别是代谢和细胞命运[1]、[2]、[3]。为了从 MIMS 图像中获得形态学相关数据，我们必须定义感兴趣区域（ROI）。ROI 通过手动绘制，这是一个繁琐且耗时的过程。我们已经开发并成功应用了支持向量机（SVM）来分割 MIMS 图像，该方法允许快速、半自动地检测感兴趣区域的边界。使用 SVM，可以为来自两个不相关数据集的两种类型的图像获得高质量的 ROI（与专家的手动勾画相比）。这种自动化简化、加速和改进了 MIMS 图像的后处理分析。该方法已集成到“Open MIMS”中，这是一个用于全面分析 MIMS 图像的 ImageJ 插件，可在 http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php 上在线获取。
