Trypan blue dye exclusion-based cell viability measurements are highly dependent upon image quality and consistency. In order to make measurements repeatable, one must be able to reliably capture images at a consistent focal plane, and with signal-to-noise ratio within appropriate limits to support proper execution of image analysis routines. Imaging chambers and imaging systems used for trypan blue analysis can be inconsistent or can drift over time, leading to a need to assure the acquisition of images prior to automated image analysis. Although cell-based autofocus techniques can be applied, the heterogeneity and complexity of the cell samples can make it difficult to assure the effectiveness, repeatability and accuracy of the routine for each measurement. Instead of auto-focusing on cells in our images, we add control beads to the images, and use them to repeatedly return to a reference focal plane. We use bead image features that have stable profiles across a wide range of focal values and exposure levels. We created a predictive model based on image quality features computed over reference datasets. Because the beads have little variation, we can determine the reference plane from bead image features computed over a single-shot image and can reproducibly return to that reference plane with each sample. The achieved accuracy (over 95%) is within the limits of the actuator repeatability. We demonstrate that a small number of beads (less than 3 beads per image) is needed to achieve this accuracy. We have also developed an open-source Graphical User Interface called Bead Benchmarking-Focus And Intensity Tool (BB-FAIT) to implement these methods for a semi-automated cell viability analyser.

中文翻译：

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使用珠子建立参考焦平面以进行基于台盼蓝的活力测量

基于台盼蓝染料排除的细胞活力测量高度依赖于图像质量和一致性。为了使测量具有可重复性，必须能够在一致的焦平面上可靠地捕获图像，并且信噪比在适当的范围内，以支持图像分析程序的正确执行。用于台盼蓝分析的成像室和成像系统可能不一致或随时间漂移，导致需要确保在自动图像分析之前获取图像。尽管可以应用基于细胞的自动对焦技术，但细胞样本的异质性和复杂性可能会导致难以保证每次测量例程的有效性、可重复性和准确性。我们不是自动聚焦图像中的细胞，而是在图像中添加控制珠，并使用它们反复返回参考焦平面。我们使用在广泛的焦点值和曝光水平上具有稳定轮廓的微珠图像特征。我们基于在参考数据集上计算的图像质量特征创建了一个预测模型。因为珠子几乎没有变化，我们可以根据在单次图像上计算的珠子图像特征确定参考平面，并且可以通过每个样本可重复地返回到该参考平面。达到的精度（超过 95%）在执行器可重复性的范围内。我们证明需要少量的珠子（每张图像少于 3 个珠子）来实现这种准确性。