Information-rich localization microscopy through machine learning

Information-rich localization microscopy through machine learning

Single-molecule methods often rely on point spread functions that are tailored to interpret specific information. Here the authors use a neural network to extract complex PSF information from experimental images, and demonstrate this by classifying color and axial positions of emitters.

Bibliographic Details
Main Authors: Taehwan Kim, Seonah Moon, Ke Xu
Format: Article
Language:English
Published: Nature Publishing Group 2019-04-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-019-10036-z
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spelling doaj-d16a8683e3b8455ab94be76774f148ad2021-05-11T11:31:31ZengNature Publishing GroupNature Communications2041-17232019-04-011011810.1038/s41467-019-10036-zInformation-rich localization microscopy through machine learningTaehwan Kim0Seonah Moon1Ke Xu2Department of Electrical Engineering and Computer Sciences, University of CaliforniaDepartment of Chemistry, University of CaliforniaDepartment of Chemistry, University of CaliforniaSingle-molecule methods often rely on point spread functions that are tailored to interpret specific information. Here the authors use a neural network to extract complex PSF information from experimental images, and demonstrate this by classifying color and axial positions of emitters.https://doi.org/10.1038/s41467-019-10036-z
collection DOAJ
language English
format Article
sources DOAJ
author Taehwan Kim
Seonah Moon
Ke Xu
spellingShingle Taehwan Kim
Seonah Moon
Ke Xu
Information-rich localization microscopy through machine learning
Nature Communications
author_facet Taehwan Kim
Seonah Moon
Ke Xu
author_sort Taehwan Kim
title Information-rich localization microscopy through machine learning
title_short Information-rich localization microscopy through machine learning
title_full Information-rich localization microscopy through machine learning
title_fullStr Information-rich localization microscopy through machine learning
title_full_unstemmed Information-rich localization microscopy through machine learning
title_sort information-rich localization microscopy through machine learning
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2019-04-01
description Single-molecule methods often rely on point spread functions that are tailored to interpret specific information. Here the authors use a neural network to extract complex PSF information from experimental images, and demonstrate this by classifying color and axial positions of emitters.
url https://doi.org/10.1038/s41467-019-10036-z
work_keys_str_mv AT taehwankim informationrichlocalizationmicroscopythroughmachinelearning
AT seonahmoon informationrichlocalizationmicroscopythroughmachinelearning
AT kexu informationrichlocalizationmicroscopythroughmachinelearning
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