Abstract: The objective of the paper is to provide a general view for automatic cup to disc ratio (CDR) assessment in fundus images. As for the cause of blindness, glaucoma ranks as the second in ocular diseases. Vision loss caused by glaucoma cannot be reversed, but the loss may be avoided if screened in the early stage of glaucoma. Thus, early screening of glaucoma is very requisite to preserve vision and maintain quality of life. Optic nerve head (ONH) assessment is a useful and practical technique among current glaucoma screening methods. Vertical CDR as one of the clinical indicators for ONH assessment, has been well-used by clinicians and professionals for the analysis and diagnosis of glaucoma. The key for automatic calculation of vertical CDR in fundus images is the segmentation of optic cup (OC) and optic disc (OD). We take a brief description of methodologies about the OC and disc optic segmentation and comprehensively presented these methods as two aspects: hand-craft feature and deep learning feature. Sliding window regression, super-pixel level, image reconstruction, super-pixel level low-rank representation (LRR), deep learning methodologies for segmentation of OD and OC have been shown. It is hoped that this paper can provide guidance and bring inspiration to other researchers. Every mentioned method has its advantages and limitations. Appropriate method should be selected or explored according to the actual situation. For automatic glaucoma screening, CDR is just the reflection for a small part of the disc, while utilizing comprehensive factors or multimodal images is the promising future direction to furthermore enhance the performance.