基于深度学习的眼底血管图像分割研究进展

眼底血管图像分割对青光眼、糖尿病视网膜病变等多种眼部疾病有较好的辅助诊断作用, 目前深度学习因其强大的抽象特征发现能力, 有望满足人们从眼底血管图像中提取特征信息进行图像自动分割的需求, 成为眼底血管图像分割领域的研究热点. 为更好把握该领域的研究进展, 本文对相关数据集和评价指标整理归纳, 对深度学习在眼底血管图像分割中的应用进行详细阐述, 重点梳理各类分割方法的基本思想、网络结构及改进之处, 分析现有眼底血管图像分割方法存在的局限性及面临的挑战, 并对该领域未来的研究方向做出展望.

Research Progress in Segmentation of Retinal Blood Vessel Images Based on Deep Learning

Retinal blood vessel image segmentation has a good auxiliary diagnostic effect on various eye diseases such as glaucoma and diabetic retinopathy. Currently, deep learning, with its powerful ability to discover abstract features, is expected to meet people’s needs for extracting feature information from retinal blood vessel images for automatic image segmentation. It has become a research hotspot in the field of retinal blood vessel image segmentation. To better grasp the research progress in this field, this study summarizes the relevant datasets and evaluation indicators and elaborates in detail on the application of deep learning in retinal blood vessel image segmentation. It focuses on the basic ideas, network structure, and improvements of various segmentation methods, analyzing the limitations and challenges faced by existing retinal blood vessel image segmentation methods and looking forward to the future research direction in this field.