Thickness evaluation and analysis of corneal layers are important for diagnosis and treatments considering corneal disease. Optical Coherence Tomography (OCT) can produce micron-scaled cross-sectional images in a non-invasive and non-contacting manner. Since manual segmentation and layer detection within such images are time-consuming, physicians prefer automatic/semi-automatic methods. This paper reviewed main and important methods of corneal layer segmentationsapplied to OCT images. The methods are compared and described in three categories: preprocessing, segmentation, and thickness mapping (layers’ topography). The purpose of preprocessing was to remove noise and artifacts from such OCT images. Studies show that methods based on Hough transform, which are consistent with the corneal arc structure when compared to graph and threshold methods, are able to extract accurate boundaries in a reasonable time. Meanwhile, artificial intelligence and the deep learning approach has opened new horizons in segmentation and analysis of such images. In studies, generally the aim was to extract and present OCT image information in a form that would help ophthalmologists better diagnose and treat corneal abnormalities; therefore it can be concluded thatlayer topography and its related issues that require automatic processing of a set of cross-sectional images, isan important output that is not addressed in many research.