Local-Region and Cross-Dataset Contrastive Learning for Retinal Vessel Segmentation

Retinal vessel segmentation is an essential preprocessing step for computer-aided diagnosis of ophthalmic diseases. Many efforts have been made to improve vessel segmentation by designing complex deep networks. However, due to some features related to detailed structures are not discriminative enough, it is still required to further improve the segmentation performance. Without adding complex network structures, we propose a local-region and cross-dataset contrastive learning method to enhance the feature embedding ability of a U-Net. Our method includes a local-region contrastive learning strategy and a cross-dataset contrastive learning strategy. The former aims to more effectively separate the features of pixels that are easily confused with their neighbors inside local regions. The latter utilizes a memory bank scheme that further enhances the features by fully exploiting the global contextual information of the whole dataset. We conducted extensive experiments on two public datasets (DRIVE and CHASE_DB1). The experimental results verify the effectiveness of the proposed method that has achieved the state-of-the-art performances.