Classification accuracy in multiple color fluorescence imaging microscopy.

Cytometry Pub Date : 2000-10-01
K R Castleman, R Eils, L Morrison, J Piper, K Saracoglu, M A Schulze, M R Speicher
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Abstract

Background: The discriminatory power and imaging efficiency of different multicolor FISH (M-FISH) analysis systems are key factors in obtaining accurate and reproducible classification results. In a recent paper, Garini et al. put forth an analytical technique to quantify the discriminatory power ("S/N ratio") and imaging efficiency ('excitation efficiency') of multicolor fluorescent karyotyping systems.

Methods: A parametric model of multicolor fluorescence microscopy, based on the Beer-Lambert law, is analyzed and reduced to a simple expression for S/N ratio. Parameters for individual system configurations are then plugged into the model for comparison purposes.

Results: We found that several invalid assumptions, which are used to reduce the complex mathematics of the Beer-Lambert law to a simple S/N ratio, result in some completely misleading conclusions about classification accuracy. The authors omit the most significant noise source, and consider only one highly abstract and unrepresentative situation. Unwisely chosen parameters used in the examples lead to predictions that are not consistent with actual results.

Conclusions: The earlier paper presents an inaccurate view of the M-FISH situation. In this short communication, we point out several inaccurate assumptions in the mathematical development of Garini et al. and the poor choices of parameters in their examples. We show results obtained with different imaging systems that indicate that reliable and comparable results are obtained if the metaphase samples are well-hybridized. We also conclude that so-called biochemical noise, not photon noise, is the primary factor that limits pixel classification accuracy, given reasonable exposure times.

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多色荧光成像显微镜的分类精度。
背景:不同多色FISH (M-FISH)分析系统的分辨能力和成像效率是获得准确、可重复分类结果的关键因素。在最近的一篇论文中,Garini等人提出了一种分析技术来量化多色荧光核型系统的鉴别能力(“信噪比”)和成像效率(“激发效率”)。方法:分析基于Beer-Lambert定律的多色荧光显微镜参数化模型,并将其简化为信噪比的简单表达式。然后将各个系统配置的参数插入到模型中,以便进行比较。结果:我们发现,一些用来将Beer-Lambert定律的复杂数学简化为简单信噪比的无效假设,导致了一些关于分类精度的完全误导的结论。作者忽略了最重要的噪声源,只考虑了一种高度抽象和不具代表性的情况。示例中使用的不明智的参数导致预测与实际结果不一致。结论:早期的论文对M-FISH的情况提出了一个不准确的观点。在这个简短的交流中,我们指出了Garini等人在数学发展中的几个不准确的假设,以及他们的例子中参数的糟糕选择。我们展示了用不同的成像系统获得的结果,表明如果中期样本杂交良好,可以获得可靠和可比的结果。我们还得出结论,所谓的生化噪声,而不是光子噪声,是限制像素分类精度的主要因素,给定合理的曝光时间。
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