STORM image denoising and information extraction

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.