Model based dynamics analysis in live cell microtubule images.
Model based dynamics analysis in live cell microtubule images.
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DOI：
10.1186/1471-2121-8-s1-s4
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发表时间：
2007-07-10
期刊：
影响因子：
--
通讯作者：
Rose K
中科院分区：
文献类型：
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作者：
Altinok A;Kiris E;Peck AJ;Feinstein SC;Wilson L;Manjunath BS;Rose K
微管的动态生长和缩短行为是微管在几乎所有真核细胞中发挥的基本作用的核心。传统上，微管的行为是通过在不同的实验条件下人工跟踪时间推移图像中的单个微管来量化的。手动分析是费力的、近似的，而且在从数据中提取潜在有价值的信息方面往往提供有限的分析能力。在这项工作中，我们提出了基于计算机视觉和机器学习的方法来从时移图像中提取新的动力学信息。利用实际的微管数据，我们估计了微管行为的统计模型，这些模型在识别微管动态行为的共同和不同特征方面非常有效。除了传统的分析方法外，计算方法还为研究微管的动态行为提供了强大的分析能力。引入了新的功能，如建立和查询微管图像数据库，以量化和分析微管的动态行为。
The dynamic growing and shortening behaviors of microtubules are central to the fundamental roles played by microtubules in essentially all eukaryotic cells. Traditionally, microtubule behavior is quantified by manually tracking individual microtubules in time-lapse images under various experimental conditions. Manual analysis is laborious, approximate, and often offers limited analytical capability in extracting potentially valuable information from the data. In this work, we present computer vision and machine-learning based methods for extracting novel dynamics information from time-lapse images. Using actual microtubule data, we estimate statistical models of microtubule behavior that are highly effective in identifying common and distinct characteristics of microtubule dynamic behavior. Computational methods provide powerful analytical capabilities in addition to traditional analysis methods for studying microtubule dynamic behavior. Novel capabilities, such as building and querying microtubule image databases, are introduced to quantify and analyze microtubule dynamic behavior.