Content-Aware AI Driven Super-Resolution Microscopy

Ricardo Henriques (primary)
Laboratory for Molecular Cell Biology
University College London and The Francis Crick Institute
Buzz Baum (secondary)
Laboratory for Molecular Cell Biology and IPLS
University College London

Abstract

Microscopy is one of the highest-impact innovations in the history of science. The recent development of Super-Resolution Microscopy has further enable the unprecedented capacity to observe cellular behaviour at near molecular-scale. However, the way we setup microscopes is still based on educated guesses by researchers with some help from empirical criteria. Furthermore, the quantification and interpretation of images, generating models on cellular behaviour is decoupled of the imaging procedure itself. This project aims take the first steps to develop artificial intelligence via deep reinforcement learning and convolution neural networks, guiding microscopes to autonomously observe cells while mathematically describing behaviour.


References

1. Gustafsson, N., Culley, S., Ashdown, G., Owen, D. M., Pereira, P. M. & Henriques, R. Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations. Nat. Commun. 7, 12471 (2016).
2. Culley, S., Albrecht, D., Jacobs, C., Pereira, P. M., Leterrier, C., Mercer, J. & Henriques, R. Quantitative mapping and minimization of super-resolution optical imaging artifacts. Nat. Methods (2018). doi:10.1038/nmeth.4605
3. Weigert, M. et al. Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy. bioRxiv 236463 (2017). doi:10.1101/236463


BBSRC Area
Genes, development and STEM* approaches to biologyMolecules, cells and industrial biotechnology
Area of Biology
BiotechnologyCell Biology
Techniques & Approaches
BioinformaticsBiophysicsEngineeringImage ProcessingMathematics / StatisticsMicroscopy / ElectrophysiologySimulation / Modelling