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Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy


Louis-Émile Robitaille, Audrey Durand, Marc-André Gardner, Christian Gagné, Paul De Koninck and Flavie Lavoie-Cardinal


Abstract - With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often difficult for non-expert users. In this work, we tackle the problem of learning the quality function of super-resolution images from scores provided by experts. More specifically, we are proposing a system based on a deep neural network that can provide a quantitative quality measure of a STED image of neuronal structures given as input. We conduct a user study in order to evaluate the quality of the predictions of the neural network against those of a human expert. Results show the potential while highlighting some of the limits of the proposed approach.



Bibtex:

@inproceedings{Robitaille1197,
    author    = { Louis-Émile Robitaille and Audrey Durand and Marc-André Gardner and Christian Gagné and Paul De Koninck and Flavie Lavoie-Cardinal },
    title     = { Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy },
    booktitle = { Innovative Applications of Artificial Intelligence (IAAI-18) },
    pages     = { 6 },
    year      = { 2018 },
    month     = { 02 }
}

Dernière modification: Nov 27 2017 11:06PM par cgagne

     
   
   

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