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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Bio-Driven Cell Region Detection in Human Embryonic Stem Cell Assay
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Bio-Driven Cell Region Detection in Human Embryonic Stem Cell Assay

机译：人类胚胎干细胞测定中的生物驱动细胞区域检测

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摘要

This paper proposes a bio-driven algorithm that detects cell regions automatically in the human embryonic stem cell (hESC) images obtained using a phase contrast microscope. The algorithm uses both statistical intensity distributions of foreground/hESCs and background/substrate as well as cell property for cell region detection. The intensity distributions of foreground/hESCs and background/substrate are modeled as a mixture of two Gaussians. The cell property is translated into local spatial information. The algorithm is optimized by parameters of the modeled distributions and cell regions evolve with the local cell property. The paper validates the method with various videos acquired using different microscope objectives. In comparison with the state-of-the-art methods, the proposed method is able to detect the entire cell region instead of fragmented cell regions. It also yields high marks on measures such as Jacard similarity, Dice coefficient, sensitivity and specificity. Automated detection by the proposed method has the potential to enable fast quantifiable analysis of hESCs using large data sets which are needed to understand dynamic cell behaviors.
机译：本文提出了一种生物驱动算法，该算法可自动检测使用相衬显微镜获得的人类胚胎干细胞（hESC）图像中的细胞区域。该算法使用前景/ hESC和背景/底物的统计强度分布以及细胞属性进行细胞区域检测。前景/ hESC和背景/底物的强度分布被建模为两个高斯的混合。单元属性被转换为局部空间信息。该算法通过建模分布的参数进行优化，并且单元区域随局部单元属性而演化。本文通过使用不同显微镜物镜采集的各种视频验证了该方法。与最先进的方法相比，该方法能够检测整个细胞区域而不是碎片化的细胞区域。它还在诸如Jacard相似度，Dice系数，敏感性和特异性等指标上获得很高的评价。通过所提出的方法进行的自动检测具有潜力，可以使用了解动态细胞行为所需的大数据集对hESC进行快速定量分析。

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