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.
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机译：多同位素成像质谱（MIMS）将二次离子质谱（SIMS）与几种原子质量的检测，稳定同位素作为标记物的使用以及附属的定量图像分析软件相关联。通过关联图像和测量，MIMS允许人们获得有关亚细胞域生物过程的定量信息。 MIMS可以应用于广泛的生物医学问题，尤其是新陈代谢和细胞命运[1]，[2]，[3]。为了从MIMS图像中获得形态相关的数据，我们必须定义感兴趣区域（ROI）。 ROI是手工绘制的，这是一个乏味且耗时的过程。我们已经开发并成功地将支持向量机（SVM）用于MIMS图像分割，从而可以快速，半自动对感兴趣区域进行边界检测。使用SVM，可以从不相关的数据集中获得2种类型的图像的高质量ROI（与专家的手动描述相比）。这种自动化功能可以简化，加速和改善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，可从http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php在线获得。
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