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A Model based Survey of Colour Deconvolution in Diagnostic Brightfield Microscopy: Error Estimation and Spectral Consideration

机译:诊断明场显微镜中基于模型的颜色反褶积调查:误差估计和光谱考虑

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摘要

Colour deconvolution is a method used in diagnostic brightfield microscopy to transform colour images of multiple stained biological samples into images representing the stain concentrations. It is applied by decomposing the absorbance values of stain mixtures into absorbance values of single stains. The method assumes a linear relation between stain concentration and absorbance, which is only valid under monochromatic conditions. Diagnostic applications, in turn, are often performed under polychromatic conditions, for which an accurate deconvolution result cannot be achieved. To show this, we establish a mathematical model to calculate non-monochromatic absorbance values based on imaging equipment typically used in histology and use this simulated data as the ground truth to evaluate the accuracy of colour deconvolution. We show the non-linear characteristics of the absorbance formation and demonstrate how it leads to significant deconvolution errors. In particular, our calculations reveal that polychromatic illumination causes 10-times higher deconvolution errors than sequential monochromatic LED illumination. In conclusion, our model can be used for a quantitative assessment of system components - and also to assess and compare colour deconvolution methods.
机译:颜色反卷积是一种用于诊断明场显微镜的方法,可以将多个已染色生物样品的彩色图像转换为代表染色剂浓度的图像。通过将污渍混合物的吸光度值分解为单个污渍的吸光度值来应用。该方法假设污渍浓度和吸光度之间存在线性关系,这仅在单色条件下有效。反过来,诊断应用通常在多色条件下执行,因此无法获得准确的去卷积结果。为了说明这一点,我们建立了一个数学模型,根据组织学中通常使用的成像设备计算非单色吸光度值,并使用此模拟数据作为基本事实来评估颜色反卷积的准确性。我们展示了吸光度形成的非线性特征,并证明了它如何导致明显的去卷积误差。特别是,我们的计算表明,与连续单色LED照明相比,多色照明引起的反卷积误差高10倍。总之,我们的模型可用于系统组件的定量评估-还可用于评估和比较颜色反卷积方法。

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