Abstract: The segmentation results of retinal fundus images can provide auxiliary diagnosis for ophthalmic diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration. Accurate segmentation of retinal blood vessels provides strong support for diagnosis, treatment, and evaluation, helping doctors better understand the patient’s eye condition. This paper reviews recent papers on fundus vessel segmentation based on deep learning, introducing the most commonly used datasets for fundus vessel segmentation and preprocessing methods. It also classifies recent model algorithms into several categories: single-network models, multi-network models, and Transformer models. This paper introduces various modules within each category of networks, discussing their advantages and limitations in handling fundus vessel segmentation tasks. These analyses help us understand the characteristics and applicable scenarios of different modules. Furthermore, this paper summarizes the retrieved model data, comparing the performance of different algorithms on the same dataset and evaluating their strengths and weaknesses based on scores obtained from the same evaluation metrics. It analyzes the reasons for the advantages of better-scoring algorithms and points out the defects of current algorithms. Finally, it summarizes numerous challenges faced by deep learning methods in retinal vessel segmentation and identifies potential directions for future development of deep learning in fundus vessel segmentation.

Key words: fundus images, blood vessel segmentation, deep learning

摘要： 视网膜眼底图像的分割结果可为糖尿病视网膜病变、青光眼和年龄相关性黄斑病等眼科疾病的诊断提供辅助。通过准确分割视网膜血管，医生能够更好地了解患者眼部状况，为诊断、治疗和评估提供有力支持。对近年来的基于深度学习的眼底血管分割论文进行回顾整理，介绍了最常用于眼底血管分割的数据集，以及预处理方式，并将近期的模型算法分为单网络模型、多网络模型以及Transformer模型几个大类。对每一类网络中所存在的各个模块文章进行了介绍分析，探讨了它们的优势以及在处理眼底血管分割任务时的局限性。这些分析有助于理解不同模块的特点和适用场景。将所检索的模型数据进行总结，通过比较不同算法模型在同一数据集上的表现，以及根据相同的评价指标获得的分数，比较各算法模型的优劣，分析分数较好算法存在优势的原因，并指出了现如今的算法所存在的缺陷，总结深度学习的方法在视网膜血管分割中面临的诸多挑战，指出了未来深度学习在眼底血管分割方面可侧重的发展方向。

关键词: 眼底图像, 血管分割, 深度学习