Microscopy Image Restoration and Downstream Analysis - recent improvements and hopes for the future

The necessity to analyze scientific images is as old as the ability to acquire such data. While this analysis did initially happen by observation only, modern microscopy techniques now enable us to image at unprecedented spatial and temporal resolutions, through the 'eyes' of many and very diverse imaging modalities.

The unfathomable amounts of data acquired in the context of biomedical research cannot any longer be analyzed by manual observation alone. Instead, algorithmic solutions are helping researchers to study and quantify large image data.

In the past years, our abilities to use artificial neural networks (ANNs) for the automated analysis of scientific image data gained significant traction, and many important analysis problems have now much improved solutions based on ANNs. At the same time, we start being aware of limitations that come with this new set of machine learning approaches.

In my talk I would like to update you on some of the latest algorithmic developments from our and other labs. More specifically, I will talk about improved but easy to use image restoration and segmentation methods and the efforts of our community to stored, share, and run ANN based methods via the BioImage Model Zoo -- an infrastructure we are currently helping to establish.

Finally, I will carefully attempt to look into the future and share my predictions about how artificial intelligence will help us make valuable scientific discoveries at elevated pace.