基于稀疏性的STORM超分辨率图像背景去除
Sparsity-based background removal for STORM super-resolution images
AI总结：
本文提出一种基于稀疏分解的STORM背景去除方法，通过无监督训练的轻量网络SLNet分离低秩背景与稀疏信号，在胶质细胞和微管数据集上实现了比传统方法更高的定位精度和重建分辨率。
摘要：
单分子定位显微技术，如随机光学重建显微术（STORM），通过随机激发稀疏闪烁的发光体来对生物样本进行成像。原始图像受到不需要的背景荧光干扰，必须去除背景才能实现超分辨率。我们通过将一个来自不同显微领域的神 经网络（SLNet）进行适配，提出了一种基于稀疏性的背景去除方法。SLNet计算图像的低秩表示，然后通过从原始图像中减去该低秩表示来计算稀疏分量，从而得到不含背景的帧。我们在两个常用的STORM数据集（一个胶质细胞数据集和一个微管数据集）上，将我们的方法与广泛使用的背景去除方法（如中值背景去除或滚球算法）进行了比较。与常用方法相比，SLNet输出的STORM帧背景更少，从而获得更高的发光体定位精度和更高分辨率的重建图像。值得注意的是，SLNet轻量且易于训练（<5分钟）。由于它以无监督方式训练，不需要任何先验信息，并且可以应用于任何STORM数据集。我们已将预训练的SLNet上传至Bioimage模型库，可通过ImageJ方便地访问。我们的结果表明，我们的稀疏分解方法可以成为一种重要且高效的STORM预处理工具。
英文摘要：
Single-molecule localization microscopy techniques, like stochastic optical reconstruction microscopy (STORM), visualize biological specimens by stochastically exciting sparse blinking emitters. The raw images suffer from unwanted background fluorescence, which must be removed to achieve super-resolution. We introduce a sparsity-based background removal method by adapting a neural network (SLNet) from a different microscopy domain. The SLNet computes a low-rank representation of the images, and then, by subtracting it from the raw images, the sparse component is computed, representing the frames without the background. We compared our approach with widely used background removal methods, such as the median background removal or the rolling ball algorithm, on two commonly used STORM datasets, one glial cell, and one microtubule dataset. The SLNet delivers STORM frames with less background, leading to higher emitters' localization precision and higher-resolution reconstructed images than commonly used methods. Notably, the SLNet is lightweight and easily trainable (<5 min). Since it is trained in an unsupervised manner, no prior information is required and can be applied to any STORM dataset. We uploaded a pre-trained SLNet to the Bioimage model zoo, easily accessible through ImageJ. Our results show that our sparse decomposition method could be an essential and efficient STORM pre-processing tool.
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