Denoising Diffusion Probabilistic Model for Retinal Image Generation and
  Segmentation
Alnur Alimanov Md Baharul Islam
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.