deepBlink: threshold-independent detection and localization of diffraction-limited spots
Author(s) -
Bastian Eichenberger,
Yinxiu Zhan,
Markus Rempfler,
Luca Giorgetti,
Jeffrey A. Chao
Publication year - 2021
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkab546
Subject(s) - spots , biology , diffraction , pattern recognition (psychology) , artificial intelligence , biological system , process (computing) , computer science , optics , physics , botany , operating system
Detection of diffraction-limited spots in single-molecule microscopy images is traditionally performed with mathematical operators designed for idealized spots. This process requires manual tuning of parameters that is time-consuming and not always reliable. We have developed deepBlink, a neural network-based method to detect and localize spots automatically. We demonstrate that deepBlink outperforms other state-of-the-art methods across six publicly available datasets containing synthetic and experimental data.
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