Automatic drusen segmentation for age-related macular degeneration in fundus images using deep learning

Drusen are the main aspect of detecting age-related macular degeneration (AMD). Ophthalmologists can evaluate the condition of AMD based on drusen in fundus images. However, in the early stage of AMD, the drusen areas are usually small and vague. This leads to challenges in the drusen segmentation task. Moreover, due to the high-resolution fundus images, it is hard to accurately predict the drusen areas with deep learning models. In this paper, we propose a multi-scale deep learning model for drusen segmentation. By exploiting both local and global information, we can improve the performance, especially in the early stages of AMD cases.