Review: Algorithmic advances in central serous chorioretinopathy OCT: From classification to segmentation

Central serous chorioretinopathy (CSC) is a common fundus disease characterized by serous retinal detachment in the macular region, which significantly impacts patients’ visual function. In recent years, with the continuous development of deep learning and image processing algorithms, remarkable progress has been made in algorithmic research based on CSC OCT images, particularly with a surge of innovative work and outstanding achievements in the areas of classification and segmentation. Through a systematic review of 62 research papers on CSC OCT algorithm development, this article summarizes multi-class algorithms such as binary and three-way classification, as well as algorithm optimization methods. It also reviews segmentation techniques for structures including serous retinal detachment (SRD), pigment epithelial detachment (PED), retinal vasculature, and the choroidal layer. Progress in CSC prediction, assessment, and assisted analysis algorithms is also summarized. Furthermore, the transition from classification to segmentation in CSC OCT algorithms is analyzed, along with the challenges and limitations in this research field. This review aims to provide a comprehensive understanding of the current state and future directions of CSC OCT image algorithm research for investigators in this domain.