AI-Driven Enhancement of Retinal Pigment Epithelial Cell Visualization Using Adaptive OCT

By Muhammad OsamaApr 18 2024Reviewed by Susha Cheriyedath, M.Sc.

In a recent article published in the journal Communications Medicine, researchers from the USA developed an innovative artificial intelligence (AI) method called parallel discriminator generative adversarial network (P-GAN) to enhance the visualization of retinal pigment epithelial (RPE) cells, which play a crucial role in vision and are implicated in various eye diseases.

Their approach utilizes adaptive optics optical coherence tomography (AO-OCT), a high-resolution imaging technique, to recover the cellular structure from a single noisy image, enabling detailed visualization of the retina at the cellular level.

Background

The RPE is a single layer of cells within the eye that supports the photoreceptors and plays a vital role in vision. Imaging the RPE cells in living human eyes can provide insights into the health and function of the retina, as well as the diagnosis and progression of various eye diseases, such as age-related macular degeneration, Stargardt disease, and Best disease. However, imaging the RPE cells is challenging due to their low intrinsic contrast and the presence of speckle noise, which arises from the interference of light scattered by the cells. Speckle noise reduces the visibility of the cellular structures and makes them hard to discern from a single image.

To overcome the low contrast and speckle noise, current AO-OCT imaging methods rely on acquiring and averaging a large number of images from the same retinal location, improving the signal-to-noise ratio and revealing the RPE cells. However, this approach has several limitations, such as increasing the acquisition time, introducing motion artifacts, and reducing the imaging throughput. Therefore, there is a need for a more efficient and reliable method to enhance the visualization of the RPE cells from AO-OCT images.

About the Research

In this paper, the authors proposed P-GAN to recover the cellular structure from a single speckled AO-OCT image without the need for multiple image acquisition and averaging. Their innovative technique is a deep learning-based generative model capable of generating realistic-looking images from noisy inputs. The model comprises three key components: a generator, a twin discriminator, and a convolutional neural network (CNN) discriminator.

The generator takes the speckled AO-OCT image as input and transforms it into an image depicting RPE cells by employing a series of convolutional layers. Concurrently, the twin discriminator compares the features of the generated image with the ground truth averaged image and provides a similarity score.

On the other hand, the CNN discriminator assigns a label of "fake" or "real" to the generated image based on its statistical distribution. Through an adversarial training process, the generator aims to fool the discriminators by producing images that closely resemble the ground truth, while the discriminators try to differentiate between the generated and actual images.

The researchers used AO-OCT images from eight eyes of seven healthy participants to train and validate P-GAN. Additionally, they utilized images from three additional participants to evaluate the performance of their method on experimental data. Moreover, they compared P-GAN with other existing AI-based methods, including the u-shaped network (U-Net), generative adversarial network (GAN), pixel-to-pixel (Pix2Pix), cycle-consisted GAN (CycleGAN), medical image translation using GAN (MedGAN), and uncertainty-guided progressive GAN (UP-GAN), using a variety of objective and subjective metrics.

Research Findings

The outcomes showed that P-GAN successfully recovered the cellular structure from the speckled images, as evidenced by the improved contrast, perceptual similarity, and structural correlation with the ground truth averaged images. It outperformed other AI-based methods in terms of both qualitative and quantitative measures, showing better visualization of the dark cell centers and bright cell surroundings of the RPE cells. Moreover, the newly presented method achieved a contrast enhancement of 3.54-fold over the speckled images.

Additionally, the authors demonstrated that their method enabled wide-scale visualization of the RPE mosaic across different retinal locations. By stitching together the recovered images from 63 overlapping locations per eye, they achieved a substantial time saving of 99-fold compared to the traditional averaging approach.

Furthermore, the researchers validated the accuracy of the recovered images by comparing them with the ground truth averaged images at 12 locations per eye, finding good agreement in terms of cell spacing, peak distinctiveness, and Voronoi analysis.

Applications

The successful integration of AI with AO-OCT goes beyond improving RPE cell visualization. It paves the way for advanced ophthalmic imaging techniques, offering enhanced contrast and reduced imaging time. This innovative approach has the potential to revolutionize diagnostic procedures in ophthalmology, enabling more precise diagnoses and personalized treatment strategies for various retinal conditions.

Moreover, the AI model P-GAN holds promise for enhancing the visualization of RPE cells in diseased eyes, which may exhibit different contrast, appearance, and size compared to healthy RPE cells. However, achieving this requires a larger dataset of diseased RPE images and consensus on image interpretation.

Conclusion

In summary, the novel AI approach proved effective and efficient in enhancing the visualization of RPE cells from a speckled image. The authors demonstrated that their approach accurately recovered the cellular structure and contrast, enabling wide-scale visualization of the RPE mosaic across different retinal locations reliably. Moreover, their method substantially reduced the time and burden of data handling associated with AO-OCT imaging, potentially transforming the current state-of-the-art ophthalmic imaging.