Roadmap on Deep Learning for Microscopy.

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities.

A microfluidic device for TEM sample preparation.

Transmission electron microscopy (TEM) allows for visualizing and analyzing viral particles and has become a vital tool for the development of vaccines and biopharmaceuticals. However, appropriate TEM sample preparation is typically done manually which introduces operator-based dependencies and can lead to unreliable results. Here, we present a capillary-driven microfluidic single-use device that prepares a TEM grid with minimal and non-critical user interaction.