RationaleMass spectrometry imaging (MSI) has been widely used in biomedical research fields. Each pixel in MSI consists of a mass spectrum that reflects the molecule feature of the tissue spot. Because MSI contains high‐dimensional datasets, it is highly desired to develop computational methods for data mining and constructing tissue segmentation maps.MethodsTo visualize different tissue regions based on mass spectrum features and improve the efficiency in processing enormous data, we proposed a computational strategy that consists of four procedures including preprocessing, data reduction, clustering, and quantitative validation.ResultsIn this study, we examined the combination of t‐distributed stochastic neighbor embedding (t‐SNE) and hierarchical clustering (HC) for MSI data analysis. Using publicly available MSI datasets, one dataset of mouse urinary bladder, and one dataset of human colorectal cancer, we demonstrated that the generated tissue segmentation maps from this combination were superior to other data reduction and clustering algorithms. Using the staining image as a reference, we assessed the performance of clustering algorithms with external and internal clustering validation measures, including purity, adjusted Rand index (ARI), Davies–Bouldin index (DBI), and spatial aggregation index (SAI). The result indicated that SAI delivered excellent performance for automatic segmentation of tissue regions in MSI.ConclusionsWe used a clustering algorithm to construct tissue automatic segmentation in MSI datasets. The performance was evaluated by comparing it with the stained image and calculating clustering validation indexes. The results indicated that SAI is important for automatic tissue segmentation in MSI, different from traditional clustering validation measures. Compared to the reports that used internal clustering validation measures such as DBI, our method offers more effective evaluation of clustering results for MSI segmentation. We envision that the proposed automatic image segmentation strategy can facilitate deep learning in molecular feature extraction and biomarker discovery for the biomedical applications of MSI.

中文翻译：

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聚类策略在质谱成像中组织区域自动分割的应用

基本原理质谱成像（MSI）已广泛应用于生物医学研究领域。MSI 中的每个像素都由反映组织点分子特征的质谱组成。由于MSI包含高维数据集，因此非常需要开发用于数据挖掘和构建组织分割图的计算方法。方法为了基于质谱特征可视化不同组织区域并提高处理海量数据的效率，我们提出了一种计算策略：包括预处理、数据缩减、聚类和定量验证四个过程。结果在本研究中，我们检查了 t 分布随机邻域嵌入 (t-SNE) 和层次聚类 (HC) 的组合用于 MSI 数据分析。使用公开的 MSI 数据集、小鼠膀胱的一个数据集和人类结直肠癌的一个数据集，我们证明了这种组合生成的组织分割图优于其他数据缩减和聚类算法。使用染色图像作为参考，我们通过外部和内部聚类验证措施评估聚类算法的性能，包括纯度、调整兰德指数（ARI）、戴维斯-布尔丁指数（DBI）和空间聚合指数（SAI）。结果表明，SAI 在 MSI 中的组织区域自动分割方面具有出色的性能。结论我们使用聚类算法在 MSI 数据集中构建组织自动分割。通过与染色图像进行比较并计算聚类验证指标来评估性能。结果表明，与传统的聚类验证方法不同，SAI 对于 MSI 中的自动组织分割非常重要。与使用 DBI 等内部聚类验证措施的报告相比，我们的方法为 MSI 分割提供了更有效的聚类结果评估。我们设想所提出的自动图像分割策略可以促进分子特征提取和生物标志物发现中的深度学习，以用于 MSI 的生物医学应用。