(Machine-)Learning to analyze in vivo microscopy: Support vector machines
(Machine-)Learning to analyze in vivo microscopy: Support vector machines
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DOI：
10.1016/j.bbapap.2017.09.013
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发表时间：
2017-11-01
影响因子：
3.2
通讯作者：
Fernandez-Gonzalez, Rodrigo
中科院分区：
文献类型：
--
作者：
Wang, Michael F. Z.;Fernandez-Gonzalez, Rodrigo
新的显微镜技术的发展，超分辨率，长期监测活生物体中的细胞和亚细胞动力学，揭示了组织发育和修复的新的基本方面。然而，新的显微镜方法提出了几个挑战。除了对数据存储的前所未有的要求外，高分辨率延时图像的分析过于复杂，无法手动完成。机器学习技术非常适合多维图像数据的（半）自动分析。特别是，支持向量机（SVM），已经成为一种有效的方法来分析从动物获得的显微镜图像。在这里，我们讨论了使用支持向量机来分析体内显微镜数据。我们介绍了支持向量机背后的数学框架，并描述了支持向量机和其他机器学习方法用于对图像数据进行分类的度量。我们讨论的背景下，细胞分割和跟踪算法的不同SVM参数的影响。最后，我们描述了支持向量机的应用是如何在酵母筛选中研究蛋白质定位，在C。elegans，或确定果蝇胚胎的发育阶段以研究基因表达动态。我们建议，支持向量机将成为中心工具，在分析复杂的图像数据，新的显微镜模式已经成为可能。这篇文章是由刘易斯凯、约翰班齐格、阿尔伯特伯吉斯和彼得蒂尔曼编辑的《加拿大生物物理学》特刊的一部分。
The development of new microscopy techniques for super-resolved, long-term monitoring of cellular and sub cellular dynamics in living organisms is revealing new fundamental aspects of tissue development and repair. However, new microscopy approaches present several challenges. In addition to unprecedented requirements for data storage, the analysis of high resolution, time-lapse images is too complex to be done manually. Machine learning techniques are ideally suited for the (semi-)automated analysis of multidimensional image data. In particular, support vector machines (SVMs), have emerged as an efficient method to analyze microscopy images obtained from animals. Here, we discuss the use of SVMs to analyze in vivo microscopy data. We introduce the mathematical framework behind SVMs, and we describe the metrics used by SVMs and other machine learning approaches to classify image data. We discuss the influence of different SVM parameters in the context of an algorithm for cell segmentation and tracking. Finally, we describe how the application of SVMs has been critical to study protein localization in yeast screens, for lineage tracing in C. elegans, or to determine the developmental stage of Drosophila embryos to investigate gene expression dynamics. We propose that SVMs will become central tools in the analysis of the complex image data that novel microscopy modalities have made possible. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.