Adaptive Optics | NEI Data Commons

Overview

The NEI Clinical and Translational Imaging Section provides custom software for handling, quantifying, and visualizing adaptive optics retinal imaging datasets.

Resources

A software package for identifying cone photoreceptors in non-confocal adaptive optics images such as split detection.

A software package for detecting cone photoreceptor cells from non-confocal split detection adaptive optics images using machine learning.

A software package for segmenting the boundaries of cone photoreceptors in non-confocal adaptive optics images such as split detection.

A software package for segmenting cone photoreceptor cells from non-confocal split detection adaptive optics images using machine learning.

RPE Detection is a software package for identifying retinal pigment epithelial (RPE) cells in adaptive optics images-enhanced indocyanine green (AO-ICG) images using machine learning.

A generative AI model for enhancing the visualization of RPE cells from a single adaptive optics optical coherence tomography acquisition.

A generative artificial intelligence (AI) framework for enhancing the visualization of cone photoreceptor cells from sparsely sampled adaptive optics optical coherence tomography (AOOCT) images.

A generative artificial intelligence (AI) model designed to enhance fluorescently labeled retinal pigment epithelial (RPE) cells obtained from late phase indocyanine green (ICG) imaging, transforming them into high-resolution, AI-ICG images.

Questions?

Please reach out to Johnny Tam at johnny@nih.gov if you have questions about these resources.

Overview

Resources

Cone Detection

Cone Detection ML

Cone Segmentation

Cone Segmentation ML

RPE Detection

P-GAN

RRTGAN

Stratified CycleGAN

Questions?

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