Artificial Intelligence Converts 2D Images Into 3D Using Deep Learning

A University of California, Los Angeles a team of researchers has used Artificial Intelligence (AI) to turn Two-Dimensional (2D) images into stacks of Virtual Three-Dimensional (3D) slices showing activity inside organisms. Using "Deep Learning Techniques", which allows scientists to precisely label parts of living cells and tissue with dyes that glow under Special Lighting. 

2d to 3d Conversion AI

In a research published in a journal Nature Methods on November 4, 2019, the group of scientists also reported that their framework to this project, called “Deep-Z,” was able to fix errors or aberrations in images, such as when a sample is tilted or curved. Further, they demonstrated that the system could take 2D images from one type of microscope and virtually create 3D images of the sample as if they were obtained by another, more advanced microscope. 

"This is a very powerful new method that is enabled by deep learning to perform 3D imaging of live specimens, with the least exposure to light, which can be toxic to samples," said senior author Aydogan Ozcan, UCLA chancellor's professor of electrical and computer engineering.

2d Images to 3d Model Deep Learning - [Deep-Z]

Deep learning techniques, the team from the University of California, Los Angeles (UCLA) devised a master technique that extends the capabilities of fluorescence microscopy, which allows scientists to precisely label parts of living cells and tissue with dyes that glow under special lighting. 
In thousands of training runs, the neural network learned how to take a 2D image and infer accurate 3D slices at different depths within a sample. Then, the framework was tested blindly - fed with images that were not part of its training, with the virtual images compared to the actual 3D slices obtained from a scanning microscope, providing an excellent match.
Converting a 2D movie of a worm to 3D, frame by frame, the researchers were able to track the activity of individual neurons within the worm body. And starting with one or two 2D images of C.  elegant taken at different depths, Deep-Z produced virtual 3D images that allowed the team to identify individual neurons within the worm, matching a scanning microscope’s 3D output, except with much less light exposure to the living organism.
The University researchers also found that Deep-Z could produce 3D images from 2D surfaces where samples were tilted or curved - even though the neural network was trained only with 3D slices that were perfectly parallel to the surface of the sample.