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 this project, the team will use their expertise in developing new imaging methods and machine learning to create new hardware and combined software. They will focus on quantitative phase imaging (ptychography) and brightfield microscopy, especially for time-lapse imaging of live cells. Working closely with partners in low- and middle-income countries (LMICs) they will develop methods that allow researchers to get more information from images using low cost equipment.
Laura said: “We’ve got a great interdisciplinary team working on this project, consisting of biologists, microscopists, mathematicians and software engineers. It’s a really exciting project that aims to tackle a key challenge of AI: building powerful models trained on large, high-resolution data sets, while keeping them compact and efficient enough for use in lower-resource settings. I am thrilled to contribute to the development of tools with the potential for far-reaching impact, helping drive research and discovery on a global scale.“
This project 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, the project aims to speed up cell biology and imaging research worldwide.