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Machine Learning for Single-Molecule Localization Microscopy: From Data Analysis to Quantification
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DOI：10.1021/acs.analchem.3c05857.png)
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Abstract
En 中文
Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.
Keywords:
SAMPLE DRIFT CORRECTION
IMAGEJ PLUG-IN
SUPERRESOLUTION MICROSCOPY
3-DIMENSIONAL LOCALIZATION
BACKGROUND ESTIMATION
CLUSTER-ANALYSIS
RESOLUTION
CELL
RECONSTRUCTION
NANOSCOPY
Journal
IF:
6.7
Papers:
4.7W
Citations:
15.9W