A review of optic disc and optic cup segmentation based on fundus images

Optic disc (OD) and optic cup (OC) segmentation is an important task in ophthalmic medicine and is crucial for aiding glaucoma screening. With the development of smart healthcare and the increase of large datasets, there is an increasing number of research efforts targeting OD and OC segmentation, making it particularly important to provide a systematic review of the latest advances in the field. This paper presents a systematic review of commonly used datasets, evaluation metrics, and related research results in the field of OD and OC segmentation. The advantages and disadvantages of segmentation techniques based on traditional and deep learning methods are comparatively analysed. In addition, this study emphasizes the importance of OD and OC segmentation efforts in smart healthcare. Despite the technological advances, the lack of generalization capability is still a major obstacle limiting its clinical application. To address this issue, this study explores unsupervised domain adaptation methods to enhance the generalization performance of segmentation techniques and provide new strategies for clinical diagnosis. Finally, this paper discusses the challenges and future research directions faced by OD and OC segmentation when applied in the medical field to help readers comprehensively grasp the research dynamics in this area. This paper offers a comprehensive review of recent advancements in optic disc and optic cup segmentation techniques crucial for glaucoma screening. It presents insights into commonly used datasets, evaluation metrics, and a comparative analysis of traditional and deep learning methods. Readers will gain a thorough understanding of the current strengths and challenges in optic disc and optic cup segmentation, enabling them to identify research gaps and guide future investigations in this field. image