Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325001, People's Republic of China.
Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325001, People's Republic of China.
Biomed Phys Eng Express. 2024 Sep 24;10(6). doi: 10.1088/2057-1976/ad7a02.
Stochastic optical reconstruction microscopy (STORM) is extensively utilized in the fields of cell and molecular biology as a super-resolution imaging technique for visualizing cells and molecules. Nonetheless, the imaging process of STORM is frequently susceptible to noise, which can significantly impact the subsequent image analysis. Moreover, there is currently a lack of a comprehensive automated processing approach for analyzing protein aggregation states from a large number of STORM images. This paper initially applies our previously proposed denoising algorithm, UNet-Att, in STORM image denoising. This algorithm was constructed based on attention mechanism and multi-scale features, showcasing a remarkably efficient performance in denoising. Subsequently, we propose a collection of automated image processing algorithms for the ultimate feature extractions and data analyses of the STORM images. The information extraction workflow effectively integrates automated methods of image denoising, objective image segmentation and binarization, and object information extraction, and a novel image information clustering algorithm specifically developed for the morphological analysis of the objects in the STORM images. This automated workflow significantly improves the efficiency of the effective data analysis for large-scale original STORM images.
随机光学重建显微镜（STORM）在细胞和分子生物学领域被广泛应用，是一种用于可视化细胞和分子的超分辨率成像技术。然而，STORM 的成像过程经常容易受到噪声的影响，这会对后续的图像分析产生重大影响。此外，目前还缺乏一种全面的自动化处理方法，用于从大量 STORM 图像中分析蛋白质聚集状态。本文首先将我们之前提出的去噪算法 UNet-Att 应用于 STORM 图像去噪。该算法基于注意力机制和多尺度特征构建，在去噪方面表现出了非常高效的性能。随后，我们提出了一系列自动化的图像处理算法，用于对 STORM 图像进行最终的特征提取和数据分析。信息提取工作流程有效地集成了图像去噪、目标图像分割和二值化、对象信息提取的自动化方法，以及专门为 STORM 图像中对象的形态分析而开发的新颖的图像信息聚类算法。这种自动化工作流程显著提高了大规模原始 STORM 图像的有效数据分析效率。
