STORM Image Denoising and Information Extraction.

IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Biomedical Physics & Engineering Express Pub Date : 2024-09-12 DOI:10.1088/2057-1976/ad7a02
Yuer Lu,Yongfa Ying,Chengliang Huang,Xiang Li,Jinyan Cheng,Rongwen Yu,Lixiang Ma,Jianwei Shuai,Xuejin Zhou,Jinjin Zhong
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Abstract

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.
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STORM 图像去噪与信息提取。
随机光学重建显微镜(STORM)作为一种超分辨率成像技术被广泛应用于细胞和分子生物学领域,用于观察细胞和分子。然而,STORM 的成像过程经常受到噪声的影响,这会严重影响后续的图像分析。此外,目前还缺乏一种全面的自动处理方法来分析大量 STORM 图像中的蛋白质聚集状态。本文首先将我们之前提出的去噪算法 UNet-Att 应用于 STORM 图像的去噪。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显著的高效性。随后,我们提出了一系列自动图像处理算法,用于 STORM 图像的最终特征提取和数据分析。信息提取工作流程有效地整合了图像去噪、客观图像分割和二值化、物体信息提取等自动化方法,以及专门为 STORM 图像中的物体形态分析开发的新型图像信息聚类算法。这一自动化工作流程大大提高了对大规模原始 STORM 图像进行有效数据分析的效率。
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来源期刊
Biomedical Physics & Engineering Express
Biomedical Physics & Engineering Express RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
2.80
自引率
0.00%
发文量
153
期刊介绍: BPEX is an inclusive, international, multidisciplinary journal devoted to publishing new research on any application of physics and/or engineering in medicine and/or biology. Characterized by a broad geographical coverage and a fast-track peer-review process, relevant topics include all aspects of biophysics, medical physics and biomedical engineering. Papers that are almost entirely clinical or biological in their focus are not suitable. The journal has an emphasis on publishing interdisciplinary work and bringing research fields together, encompassing experimental, theoretical and computational work.
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