A machine learning approach for online automated optimization of super-resolution optical microscopy
Complex imaging systems like super-resolution microscopes currently require laborious parameter optimization before imaging. Here, the authors present an imaging optimization framework based on machine learning that performs simultaneous parameter optimization to simplify this procedure for a wide r...
Main Authors: | Audrey Durand, Theresa Wiesner, Marc-André Gardner, Louis-Émile Robitaille, Anthony Bilodeau, Christian Gagné, Paul De Koninck, Flavie Lavoie-Cardinal |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2018-12-01
|
Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-018-07668-y |
Similar Items
-
Réseaux de neurones pour l'apprentissage de la préférence en microscopie super-résolution
by: Robitaille, Louis-Emile
Published: (2021) -
Activity-Dependent Remodeling of Synaptic Protein Organization Revealed by High Throughput Analysis of STED Nanoscopy Images
by: Theresa Wiesner, et al.
Published: (2020-10-01) -
Super-Resolution Microscopy of Chromatin
by: Udo J. Birk
Published: (2019-06-01) -
Super-Resolution Microscopy of Synaptic Proteins
by: Aufmkolk, Sarah
Published: (2018) -
New approaches in super-resolution microscopy
by: Yang, Bin
Published: (2015)