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


  • Louis-Émile Robitaille Université Laval
  • Audrey Durand Université Laval
  • Marc-André Gardner Université Laval
  • Christian Gagné Université Laval
  • Paul De Koninck Université Laval
  • Flavie Lavoie-Cardinal Université Laval


deep neural network regression, super-resolution microscopy, image quality


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.




How to Cite

Robitaille, L.- Émile, Durand, A., Gardner, M.-A., Gagné, C., De Koninck, P., & Lavoie-Cardinal, F. (2018). Learning to Become an Expert: Deep Networks Applied to Super-Resolution Microscopy. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11426