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.

Optical-trapping of particles in air using parabolic reflectors and a hollow laser beam.

We present an advanced optical-trapping method that is capable of trapping arbitrary shapes of transparent and absorbing particles in air. Two parabolic reflectors were used to reflect the inner and outer parts of a single hollow laser beam, respectively, to form two counter-propagating conical beams and bring them into a focal point for trapping. This novel design demonstrated high trapping efficiency and strong trapping robustness with a simple optical configuration.