New AI Technique Generates Clear Images of Thick Biological Samples

MBL Whitman Fellows Hari Shroff, Patrick LaRivière and Daniel Colón-Ramos met for many summers in Woods Hole to discuss deep learning approaches to improve resolution in fluorescence microscopy. This new publication in Nature Communications grew from those discussions.

Depth degradation is a problem biologists know all too well: The deeper you look into a sample, the fuzzier the image becomes. A worm embryo or a piece of tissue may only be tens of microns thick, but the bending of light causes microscopy images to lose their sharpness as the instruments peer beyond the top layer.

To deal with this problem, microscopists add technology to existing microscopes to cancel out these distortions. But this technique, called adaptive optics, requires time, money, and expertise, making it available to relatively few biology labs.

Now, researchers at Janelia have developed a way to make a similar correction, but without using adaptive optics, adding additional hardware, or taking more images. A team from the Shroff Lab has developed a new AI method that produces sharp microscopy images throughout a thick biological sample. Read rest of the article here.

Source: New AI technique generates clear images of thick biological samples without the fancy hardware | HHMI News