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PunctaFinder:一种用于荧光显微镜图像中自动斑点检测的算法。

PunctaFinder: An algorithm for automated spot detection in fluorescence microscopy images.

机构信息

Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, 9747 AG, Groningen, The Netherlands.

Department of Biomedical Sciences, University of Groningen, University Medical Center Groningen, 9713 AV Groningen, The Netherlands.

出版信息

Mol Biol Cell. 2024 Dec 1;35(12):mr9. doi: 10.1091/mbc.E24-06-0254. Epub 2024 Nov 13.

DOI:10.1091/mbc.E24-06-0254
PMID:39535892
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11656481/
Abstract

Fluorescence microscopy has revolutionized biological research by enabling the visualization of subcellular structures at high resolution. With the increasing complexity and volume of microscopy data, there is a growing need for automated image analysis to ensure efficient and consistent interpretation. In this study, we introduce PunctaFinder, a novel Python-based algorithm designed to detect puncta, small bright spots, in raw fluorescence microscopy images without image denoising or signal enhancement steps. Furthermore, unlike other available spot detectors, PunctaFinder not only detects puncta, but also defines the cytoplasmic region, making it a valuable tool to quantify target molecule localization in cellular contexts. PunctaFinder is a widely applicable punctum detector and size estimator, as evidenced by its successful detection of Atg9-positive vesicles, lipid droplets, aggregates of a destabilized luciferase mutant, and the nuclear pore complex. Notably, PunctaFinder excels in detecting puncta in images with a relatively low resolution and signal-to-noise ratio, demonstrating its capability to identify dim puncta and puncta of dynamic target molecules. PunctaFinder reliably detects puncta in fluorescence microscopy images where automated analysis was not possible before, providing researchers with an efficient and robust method for punctum quantification in fluorescence microscopy images.

摘要

荧光显微镜通过实现亚细胞结构的高分辨率可视化,彻底改变了生物学研究。随着显微镜数据的日益复杂和庞大,对自动化图像分析的需求也越来越大,以确保高效和一致的解释。在这项研究中,我们引入了 PunctaFinder,这是一种基于 Python 的新算法,旨在在没有图像去噪或信号增强步骤的情况下,检测原始荧光显微镜图像中的小点,即小亮点。此外,与其他可用的斑点检测器不同,PunctaFinder 不仅可以检测斑点,还可以定义细胞质区域,使其成为在细胞环境中定量目标分子定位的有价值的工具。PunctaFinder 是一种广泛适用的斑点检测器和大小估计器,其成功检测 Atg9 阳性囊泡、脂质滴、不稳定荧光素酶突变体的聚集体以及核孔复合物就是证明。值得注意的是,PunctaFinder 能够在分辨率和信噪比相对较低的图像中检测到斑点,证明了它能够识别暗淡的斑点和动态目标分子的斑点。PunctaFinder 能够可靠地检测荧光显微镜图像中的斑点,以前这些图像无法进行自动分析,为研究人员提供了一种在荧光显微镜图像中进行斑点定量的高效、稳健的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1822/11656481/95e0d2e85912/mbc-35-mr9-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1822/11656481/f073691e73b6/mbc-35-mr9-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1822/11656481/3b035c3d5e1c/mbc-35-mr9-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1822/11656481/11abc69a7faa/mbc-35-mr9-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1822/11656481/cdcc10981ecb/mbc-35-mr9-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1822/11656481/95e0d2e85912/mbc-35-mr9-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1822/11656481/f073691e73b6/mbc-35-mr9-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1822/11656481/3b035c3d5e1c/mbc-35-mr9-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1822/11656481/11abc69a7faa/mbc-35-mr9-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1822/11656481/cdcc10981ecb/mbc-35-mr9-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1822/11656481/95e0d2e85912/mbc-35-mr9-g005.jpg

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