Article
Version 1
Preserved in Portico This version is not peer-reviewed
Artificial Intelligence for Microscopy: What You Should Know
Version 1
: Received: 30 January 2019 / Approved: 1 February 2019 / Online: 1 February 2019 (09:00:39 CET)
Version 2 : Received: 15 February 2019 / Approved: 19 February 2019 / Online: 19 February 2019 (12:20:04 CET)
Version 2 : Received: 15 February 2019 / Approved: 19 February 2019 / Online: 19 February 2019 (12:20:04 CET)
A peer-reviewed article of this Preprint also exists.
Abstract
Artificial Intelligence based on Deep Learning is opening new horizons in Biomedical research and promises to revolutionize the Microscopy field. Slowly, it now transitions from the hands of experts in Computer Sciences to researchers in Cell Biology. Here, we introduce recent developments in Deep Learning applied to Microscopy, in a manner accessible to non-experts. We overview its concepts, capabilities and limitations, presenting applications in image segmentation, classification and restoration. We discuss how Deep Learning shows an outstanding potential to push the limits of Microscopy, enhancing resolution, signal and information content in acquired data. Its pitfalls are carefully discussed, as well as the future directions expected in this field.
Keywords
artificial intelligence; machine learning; live-cell imaging; super-resolution microscopy; classification; segmentation
Subject
Biology and Life Sciences, Cell and Developmental Biology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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