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用于质谱成像的自动组织检测

James Denholm^1,2, Lucy E Flint¹, Jack Richings¹

¹Integrated Bioanlaysis, Clinical Pharmacology and Safety Sciences (CPSS), AstraZeneca R&D, Cambridge CB4 0WG, U.K.

Journal of the American Society for Mass Spectrometry

|November 25, 2025

概括

我们开发了一种用于在质谱成像 (MSI) 中检测组织的自动化方法. 这种方法使用组织图像和卷积神经网络,显著改善MSI分析的组织划分.

科学领域:

生物医学成像技术生物医学成像技术
计算病理学计算病理学
分析化学分析化学

背景情况:

质谱成像 (MSI) 能够在组织中绘制生物分子的空间映射.
在MSI中手动组织划分是耗时且主观的.
需要自动化方法来简化MSI数据预处理.

研究的目的:

提出一个端到端的方法,用于在质谱图像 (MSI) 中自动检测组织.
开发一个可靠的模型,用于MSI数据中准确的组织细分.
减少MSI分析工作流程中的手动干预.

主要方法:

利用来自同一组织部分的配对MSI和组织图像.
使用QuPath软件进行注释的组织组织面膜.
通过使用地标的亲属转换将组织学面具映射到MSI空间.
开发了代谢物独立的MSI表示 (总离子电流,根-平均-平方,香农).
训练了一个卷积神经网络 (CNN) 用于组织检测.

主要成果:

实现了高交叉验证性能:准确性 (0.953),精度 (0.939),回忆 (0.923),以及索伦森-迪斯 (0.930).
在各种研究的未见测试数据上验证了模型,产生准确性 (0.945),精度 (0.965),回忆 (0.915),和Sørensen-Dice (0.935).
在各种组织类型,有机体和空间分辨率中证明了模型的稳定性.

结论:

提出的基于CNN的方法有效地自动化了MSI中的组织检测.
这种方法显著提高了MSI数据预处理的效率和客观性.
开发的技术适用于各种MSI数据集,促进更广泛的研究应用.

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