AI makes retinal imaging 100 times faster
Researchers at the National Eye Institute report that with AI, imaging cells in the retina is 100 times faster and improves image contrast 3.5-fold. They developed a novel AI-based method called parallel discriminator generative adversarial network (P-GAN)—a deep learning algorithm. The advance, they say, will provide researchers with a better tool to evaluate age-related macular degeneration (AMD) and other retinal diseases.
“Artificial intelligence helps overcome a key limitation of imaging cells in the retina, which is time,” said Johnny Tam, Ph.D., who leads the Clinical and Translational Imaging Section at NIH's National Eye Institute.
Taking OCT-based imaging to the next level
Tam is developing a technology called adaptive optics (AO) to improve imaging devices based on optical coherence tomography (OCT). “Adaptive optics takes OCT-based imaging to the next level,” said Tam. “It’s like moving from a balcony seat to a front row seat to image the retina. With AO, we can reveal 3D retinal structures at cellular-scale resolution, enabling us to zoom in on very early signs of disease.” While adding AO to OCT provides a much better view of cells, processing AO-OCT images after they’ve been captured takes much longer than OCT without AO.
Focus on the retinal pigment epithelium
Tam’s latest work targets the retinal pigment epithelium (RPE). Scientists are interested in the RPE because many diseases of the retina occur when the RPE breaks down.
Imaging RPE cells with AO-OCT comes with new challenges, including a phenomenon called speckle. Speckle interferes with AO-OCT the way clouds interfere with aerial photography. At any given moment, parts of the image may be obscured. Managing speckle is somewhat similar to managing cloud cover. Researchers repeatedly image cells over a long period of time. As time passes, the speckle shifts, which allows different parts of the cells to become visible. The scientists then undertake the laborious and time-consuming task of piecing together many images to create an image of the RPE cells that's speckle-free.
Tam and his team developed a novel AI-based method called parallel discriminator generative adversarial network (P-GAN)—a deep learning algorithm. By feeding the P-GAN network nearly 6,000 manually analyzed AO-OCT-acquired images of human RPE, each paired with its corresponding speckled original, the team trained the network to identify and recover speckle-obscured cellular features.
P-GAN outperformed other AI techniques
When tested on new images, P-GAN successfully de-speckled the RPE images, recovering cellular details. With one image capture, it generated results comparable to the manual method, which required the acquisition and averaging of 120 images. With a variety of objective performance metrics that assess things like cell shape and structure, P-GAN outperformed other AI techniques. Vineeta Das, Ph.D., a postdoctoral fellow in the Clinical and Translational Imaging Section at NEI, estimates that P-GAN reduced imaging acquisition and processing time by about 100-fold. P-GAN also yielded greater contrast, about 3.5 greater than before.
Parallel discriminator generative adversarial network (P-GAN) enabled wide-scale visualization of the retinal pigment epithelial (RPE) cellular mosaic:
Visualization of the RPE mosaic using the P-GAN recovered images (this montage was manually constructed from up to 63 overlapping recovered RPE images from the left eye of participant S2). The white squares (a–e) indicate regions that are further magnified for better visualization at retinal locations a 0.3 mm, b 0.8 mm, c 1.3 mm, d 1.7 mm, and e 2.4 mm temporal to the fovea, respectively. Credit: Vineeta Das, National Eye Institute.
By integrating AI with AO-OCT, Tam believes that a major obstacle for routine clinical imaging using AO-OCT has been overcome, especially for diseases that affect the RPE, which has traditionally been difficult to image.
“Our results suggest that AI can fundamentally change how images are captured,” said Tam. “Our P-GAN artificial intelligence will make AO imaging more accessible for routine clinical applications and for studies aimed at understanding the structure, function, and pathophysiology of blinding retinal diseases. Thinking about AI as a part of the overall imaging system, as opposed to a tool that is only applied after images have been captured, is a paradigm shift for the field of AI.”
Source: National Eye Institute
Reference: Vineeta Das, Furu Zhang, Andrew Bower, et al. Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography. Communications Medicine. April 10, 2024, https://doi.org/10.1038/s43856-024-00483-1