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.

翻译：专家利用视网膜图像及血管树来检测和诊断多种眼部、血液循环及脑部相关疾病。然而，手动分割视网膜图像是一项耗时且需要高度专业知识的过程，同时因隐私问题而困难重重。目前已提出多种图像分割方法，但大规模视网膜图像数据集的缺乏限制了这些方法的性能。一些方法基于生成对抗网络（GAN）构建深度学习模型的合成技术，以生成有限样本类别。本文提出一种新颖的去噪扩散概率模型（DDPM），其在图像合成中表现优于生成对抗网络。我们构建了包含视网膜图像及对应血管树的视网膜树（ReTree）数据集，并基于DDPM开发了一个使用ReTree数据集图像训练的分割网络。第一阶段，我们开发了一个两阶段DDPM，用于从服从标准正态分布的随机数生成血管树。随后，引导模型从给定血管树和随机分布生成眼底图像。所提出的数据集已通过定量和定性评估。定量评估指标包括Fréchet初始距离（FID）分数、Jaccard相似系数、Cohen's kappa、马修斯相关系数（MCC）、精确率、召回率、F1分数及准确率。我们使用合成数据训练血管分割模型以验证数据集的有效性，并在真实数据上进行测试。所开发的数据集及源代码可于 https://github.com/AAleka/retree 获取。