End-to-End Adversarial Retinal Image Synthesis.

Area of Science:

• Medical image analysis
• Artificial intelligence
• Computer vision

Background:

• Annotated medical data is critical but scarce for training AI models.
• Retinal image analysis requires large datasets, which are difficult and expensive to acquire.
• Synthesizing realistic medical images can overcome data limitations.

Purpose of the Study:

• To develop an end-to-end system for synthesizing retinal color images and their corresponding vessel networks.
• To address the challenge of limited annotated medical data using adversarial learning techniques.
• To create a method for generating anatomically consistent and visually plausible retinal images.

Main Methods:

• Implementation of an adversarial autoencoder for retinal vessel network synthesis.
• Utilizing a generative adversarial network (GAN) for color retinal image generation, conditioned on synthesized vessel trees.
• Jointly training both models with differentiable loss functions for an end-to-end system.
• Leveraging a latent space with a defined semantic structure for data manipulation and interpolation.

Main Results:

• Successful synthesis of retinal color images with corresponding vessel networks.
• Demonstration of an end-to-end system capable of generating unlimited retinal images.
• Evidence of a well-defined semantic structure within the learned latent space, enabling image interpolation.
• Synthesized images are distinct from training data, anatomically consistent, and of reasonable visual quality.

Conclusions:

• Adversarial learning, specifically using an adversarial autoencoder and GAN, is effective for synthesizing retinal images and vessel networks.
• The proposed system overcomes the scarcity of annotated medical data by generating high-quality synthetic retinal images.
• The learned latent space facilitates novel applications such as smooth interpolation between retinal images, showcasing semantic understanding.