Imaging ONEWORLD - 'From images to information: enhancing resolution and improving accuracy' - Dr Susan Cox

Scientific Organisers: Stefanie Reichelt, Alex Sossick, Nick Barry, Alessandro Esposito and Kirti Prakash

The meeting will begin at 1pm BST.

As part of the 'Imaging ONEWORLD' series, the focus of these lectures is on microscopy and image analysis methods and how to apply these to your research. Almost all aspects of imaging such as sample preparation, labelling strategies, experimental workflows, ‘how-to’ image and analyse, as well as facilitating collaborations and inspiring new scientific ideas will be covered. Speakers will be available for questions and answers. The organisers, CRUK CI core facility staff, Gurdon Institute, MRC-LMB, MRC Cancer Unit and NPL will be able to continue the discussion and provide advice on your imaging projects.


  • Dr Susan Cox

    King's College, London 

    Dr Susan Cox works at the Randall Centre for Cell and Molecular Biophysics, developing fluorescence microscopy techniques and applying them to discover new cell biology at the nanoscale. Her work has been recognised with the award of the Royal Microscopical Society light microscopy medal and the Society of Experimental Biology Presidents Medal. She has developed techniques to accelerate localisation microscopy, and explored the limits of localisation microscopy in terms of speed and accuracy. 

Speaker's Abstract

Fluorescence microscopy SMLM microscopy can provide huge amounts of high resolution information about a sample. Deducing whether that information is accurate, and making best use of the information, is a major challenge. Here we discuss two major challenges. First, assessing the quality of information, and in particular the potential presence of sharpening artifacts. We have developed HAWKMAN, an image assessment tool which allows the lengthscale of sharpening artifacts to be identified. Second, it is possible to synthesise information from multiple images to improve the quality of reconstructed structures. We will discuss the potential of deep learning to accelerate model-free fitting.