Department of Computer Science, Emory University, Atlanta, GA 30322, USA.
Department of Microbiology, Forsyth Institute, Cambridge, MA 02142, USA.
Bioinformatics. 2020 Feb 1;36(3):910-917. doi: 10.1093/bioinformatics/btz674.
Spectral unmixing methods attempt to determine the concentrations of different fluorophores present at each pixel location in an image by analyzing a set of measured emission spectra. Unmixing algorithms have shown great promise for applications where samples contain many fluorescent labels; however, existing methods perform poorly when confronted with autofluorescence-contaminated images.
We propose an unmixing algorithm designed to separate fluorophores with overlapping emission spectra from contamination by autofluorescence and background fluorescence. First, we formally define a generalization of the linear mixing model, called the affine mixture model (AMM), that specifically accounts for background fluorescence. Second, we use the AMM to derive an affine nonnegative matrix factorization method for estimating fluorophore endmember spectra from reference images. Lastly, we propose a semi-blind sparse affine spectral unmixing (SSASU) algorithm that uses knowledge of the estimated endmembers to learn the autofluorescence and background fluorescence spectra on a per-image basis. When unmixing real-world spectral images contaminated by autofluorescence, SSASU greatly improved proportion indeterminacy as compared to existing methods for a given relative reconstruction error.
The source code used for this paper was written in Julia and is available with the test data at https://github.com/brossetti/ssasu.
光谱解混方法通过分析一组测量的发射光谱，试图确定图像中每个像素位置存在的不同荧光团的浓度。解混算法在含有许多荧光标记的样本的应用中显示出了巨大的潜力；然而，现有的方法在面对受自发荧光污染的图像时表现不佳。
我们提出了一种解混算法，旨在从自发荧光和背景荧光的污染中分离具有重叠发射光谱的荧光团。首先，我们正式定义了线性混合模型的推广，称为仿射混合模型（AMM），该模型特别考虑了背景荧光。其次，我们使用 AMM 从参考图像中导出一种仿射非负矩阵分解方法来估计荧光团端元光谱。最后，我们提出了一种半盲稀疏仿射光谱解混（SSASU）算法，该算法利用估计的端元知识，根据每张图像学习自发荧光和背景荧光光谱。在解混受自发荧光污染的真实光谱图像时，与现有的方法相比，SSASU 在给定的相对重建误差下大大提高了比例不确定性。
本文使用的源代码是用 Julia 编写的，测试数据可在 https://github.com/brossetti/ssasu 上获得。
