Abstract
Optical coherence tomography (OCT) is an important ophthalmic imaging technique, which can generate high-resolution anatomical images and plays an important role in the detection of retinal biomarkers. However, the appearance of retinal biomarkers is complex, and some of these biomarkers differ greatly among different categories, while many features are similar. In addition, the boundaries of retinal biomarkers are often indistinguishable from the background. In this study, we propose a self-supervised contrastive boundary consistency network (SCB-Net) to detect retinal biomarkers in OCT images. A self-supervised contrastive classification module is proposed to improve the classification ability of the network between different categories of retinal biomarkers. Furthermore, in order to make the boundary of the retinal biomarkers located by the network closer to the ground truth, the boundary consistency is added on the basis of the original regressor to jointly constrain the boundary localization. The experimental results on a local dataset show that our proposed SCB-Net method achieves good detection performance compared with other detection methods.