关键词: 眼底图像, 血管分割, 深度学习

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