Improving Confocal Microscopes with Artificial Intelligence | AZO Optics
Confocal microscope. Credit: Elizaveta Galitckaia/Shutterstock.com
This article delves into a Deep Learning-aided confocal platform developed by MBL researchers, who recently reported on it in Nature.
Confocal microscopes rely on imaging a focused beam of light into a sample. A key advantage of this type of microscopy is its ability to record images without the presence of a fluorescence background.1 This means confocal microscopy can be used for thicker samples and is a label-free technique that reduces sample preparation time.
Confocal microscopy is relatively inexpensive. However, the spatial resolution achieved with the technique has been diffraction-limited – meaning structures smaller than 250-300 nm cannot be resolved in confocal microscopes using visible light sources. This requires the use of super-resolution methods, which are more commonly implemented for fluorescence-based methods. Read more ...
Source: Improving Confocal Microscopes with Artificial Intelligence | AZO Optics