Semi-blind sparse affine spectral unmixing of autofluorescence-contaminated micrographs
Semi-blind sparse affine spectral unmixing of autofluorescence-contaminated micrographs
复制标题
自体荧光污染显微图像的半盲稀疏仿射光谱分解
DOI：
10.1093/bioinformatics/btz674
复制
发表时间：
2020-02-01
期刊：
影响因子：
5.8
通讯作者：
Nagy, James G.
中科院分区：
文献类型：
--
作者：
Rossetti, Blair J.;Wilbert, Steven A.;Nagy, James G.
动机：光谱解混方法试图通过分析一组测量的发射光谱来确定图像中每个像素位置处存在的不同荧光团的浓度。解混算法已显示出很大的希望，样品中含有许多荧光标签的应用程序，然而，现有的方法表现不佳时，面对自体荧光污染的images.Results：我们提出了一个解混算法，旨在分离荧光团重叠的发射光谱污染的自体荧光和背景荧光。首先，我们正式定义了一个泛化的线性混合模型，称为仿射混合模型（AMM），特别是占背景荧光。其次，我们使用AMM推导出一个仿射非负矩阵分解方法，用于从参考图像中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，该算法使用估计端元的知识来学习每个图像的自发荧光和背景荧光光谱。当解混被自体荧光污染的真实世界光谱图像时，SSASU与现有方法相比，对于给定的相对重建误差，大大改善了比例不确定性。
Motivation: 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.Results: 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.