Friday, 30 March 2018

Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy. (arXiv:1803.10806v1 [cs.CV])

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.



from cs updates on arXiv.org https://ift.tt/2GWXIwS
//

Related Posts:

0 comments:

Post a Comment