评价用于质谱成像数据分析的数字图像识别方法。
Evaluation of Digital Image Recognition Methods for Mass Spectrometry Imaging Data Analysis.
机构信息
FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA.
Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, 27695, USA.
出版信息
J Am Soc Mass Spectrom. 2018 Dec;29(12):2467-2470. doi: 10.1007/s13361-018-2073-0. Epub 2018 Oct 15.
Abstract
Analyzing mass spectrometry imaging data can be laborious and time consuming, and as the size and complexity of datasets grow, so does the need for robust automated processing methods. We here present a method for comprehensive, semi-targeted discovery of molecular distributions of interest from mass spectrometry imaging data, using widely available image similarity scoring algorithms to rank images by spatial correlation. A fast and powerful batch search method using a MATLAB implementation of structural similarity (SSIM) index scoring with a pre-selected reference distribution is demonstrated for two sample imaging datasets, a plant metabolite study using Artemisia annua leaf, and a drug distribution study using maraviroc-dosed macaque tissue. Graphical Abstract ᅟ.
摘要
分析质谱成像数据可能既费力又耗时，而且随着数据集的规模和复杂性的增加，对强大的自动化处理方法的需求也在增加。在这里，我们提出了一种从质谱成像数据中全面、半靶向地发现感兴趣的分子分布的方法，使用广泛可用的图像相似性评分算法，根据空间相关性对图像进行排名。本文展示了一种快速而强大的批量搜索方法，该方法使用 MATLAB 实现的结构相似性（SSIM）指数评分，结合预选参考分布，对两个样本成像数据集进行搜索，一个是使用青蒿叶的植物代谢物研究，另一个是使用马拉维若布处理的猕猴组织的药物分布研究。
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