青光眼是以视神经损伤、特征性视野损伤为特点的一类眼病，在早期很难诊断，尽早发现可更好地遏制青光眼病症的恶化，降低致盲率。视盘和视杯的比值是评价青光眼诊断中的重要指标之一，视盘和视杯的分割是青光眼诊断的关键步骤。但眼底彩照中的渗出物、不均匀照明区域等特征使其可能出现相似的亮度区域，导致视盘和视杯的分割非常困难。因此本文对现有眼底彩照中视盘和视杯的分割方法进行了总结，并将其分为5大类：水平集法、模态法、能量泛函法、划分法以及基于机器学习的混合法。系统地梳理了各类算法的代表性方法，以及基本思想、理论基础、关键技术、框架流程和优缺点等。同时，概括了适用于青光眼诊断的各种数据集，包括数据集的名称、来源以及详细内容，并总结了在各种数据集中不同视盘和视杯分割结果和诊断青光眼的量化指标及其相关结果。在现有的视盘和视杯分割方法中，许多图像处理和机器学习技术得到广泛应用。通过对该领域研究算法进行综述，清晰直观地总结了各类算法之间的特点及联系，有助于推动视盘和视杯分割在青光眼疾病临床诊断中的应用。可以在很大程度上提高临床医生的工作效率，为临床诊断青光眼提供了重要的理论研究意义和价值。

Glaucoma is a kind of human-related eyes disease derived from optic nerve and vision barrier. In most cases, the drainage system of human eyes is blocked and the liquid cannot be passed through, so the produced pressure will change the optic nerve in the eyes and lead to the collapse of the visual acuity. Although it is incurable, the progression of optic nerve injury can be preserved through intraocular pressure decreasing medication and surgery. Therefore, it is essential to prevent vision loss and even blindness for patients in early stage detection and timely treatment. In addition, the main manifestations of glaucoma are the enlargement of optic disc depression and the change of optic cup morphology, so the ratio of the optic disc to optic cup is one of the most important indexes in evaluating glaucoma screening. Nowadays, the segmentation of optic disc and optic cup has become an important part of the medical image field and has been widely concerned for a long time. However, the features of the fundus color make the potential to produce similar brightness areas, leading to the quite division difficulty of the optic disc and the optic cup. In the actual segmentation process, the accuracy and robustness of the optic disc and the optic cup segmentation can be guaranteed by dealing with the effects of the feature accurately and timely. Therefore, we summarized the existing methods of the optic disc and optic cup segmentation of retinal images. Our methods are divided into five categories like horizontal set, modal, energy functional, partition, and the hybrid contexts based on machine learning. At the same time, the machanism of each scheme are summarized and analyzed like the its basicconcepts, the theoretical basis, the key technologies, the framework flow, the advantages and the disadvantages. We carry out a detailed analysis and description like typical data sets, which are suitable for glaucoma diagnosis. Specifically, it is related to the name of the data set, the source, and the features of retinal images involved. To evaluate the segmentation results and the diagnosis of glaucoma, we faciliated the calculation methods of some important quantitative index parameters, such as cup-to-disc ratio(COR), glaucoma risk index(GRI) and neural retinal edge ratio. Moreover, quantification index of a various of segmentation results of the optic disc and optic cup in multiple data sets (i.e., relative area difference, overlap area ratio, and non-overlapping area ratio, Dice measurement, accuracy) and the quantitative indicators for diagnosis of glaucoma (i.e., CDR error, average error, root mean square error) are summarized. Thanks to the continuous development of deep learning technology glaucoma diagnostic technology has become possible to obtain more precise segmentation through continuous training. Many image processing and machine learning techniques are widely used in the existing optic disc and optic cup segmentation methods. We demonstrate the diagnosis of glaucoma research algorithms to review the features and links between various algorithms. It is beneficial to promote the application of optic disc and optic cup segmentation in the clinical screening of glaucoma diseases further. Additionally, it can improve the work efficiency of clinicians, which provides an important theoretical research significance for the clinical diagnosis of glaucoma.