Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentation
This work addresses the need for large, diverse retinal image datasets to improve automated segmentation for disease diagnosis, though it is incremental by applying an existing DDPM method to a new domain-specific dataset.
The paper tackles the problem of limited retinal image datasets for vessel segmentation by proposing a Denoising Diffusion Probabilistic Model (DDPM) to generate synthetic retinal images and vessel trees, which outperformed GANs in image synthesis and enabled training a segmentation model that was validated on authentic data with quantitative metrics like FID score and F1-score.
Experts use retinal images and vessel trees to detect and diagnose various eye, blood circulation, and brain-related diseases. However, manual segmentation of retinal images is a time-consuming process that requires high expertise and is difficult due to privacy issues. Many methods have been proposed to segment images, but the need for large retinal image datasets limits the performance of these methods. Several methods synthesize deep learning models based on Generative Adversarial Networks (GAN) to generate limited sample varieties. This paper proposes a novel Denoising Diffusion Probabilistic Model (DDPM) that outperformed GANs in image synthesis. We developed a Retinal Trees (ReTree) dataset consisting of retinal images, corresponding vessel trees, and a segmentation network based on DDPM trained with images from the ReTree dataset. In the first stage, we develop a two-stage DDPM that generates vessel trees from random numbers belonging to a standard normal distribution. Later, the model is guided to generate fundus images from given vessel trees and random distribution. The proposed dataset has been evaluated quantitatively and qualitatively. Quantitative evaluation metrics include Frechet Inception Distance (FID) score, Jaccard similarity coefficient, Cohen's kappa, Matthew's Correlation Coefficient (MCC), precision, recall, F1-score, and accuracy. We trained the vessel segmentation model with synthetic data to validate our dataset's efficiency and tested it on authentic data. Our developed dataset and source code is available at https://github.com/AAleka/retree.