Removing barriers to advanced imaging and machine learning-based analysis

Over the past decade, imaging and image analysis technologies have advanced significantly, but not everyone around the world has benefited equally. Some research groups are trying to make high-quality microscope hardware and software more affordable, but there is still a lot to do. Brightfield microscopy, a common technique, is used in high and low resource settings throughout the world. Recent improvements in hardware and software have made it possible to get more information from these microscopes.

In our project, we plan to use our expertise in developing new imaging methods and machine learning to create new hardware and combined software. We will focus on quantitative phase imaging (ptychography) and brightfield microscopy, especially for time-lapse imaging of live cells. We will work closely with partners in low- and middle-income countries (LMICs) to develop methods that allow researchers to get more information from images using low cost equipment. 
 

We will set up two ptychography hubs in South America and Africa to provide access to equipment and collaborate on developing a low-cost imaging and analysis system. By improving and sharing brightfield microscopy and ptychography hardware and deep learning algorithms, we aim to speed up cell biology and imaging research worldwide.