Multi-Modal Self-Supervised Pre-Training For Joint Optic Disc And Cup Segmentation In Eye Fundus Images

This paper presents a novel approach for the segmentation of the optic disc and cup in eye fundus images using deep learning. The accurate segmentation of these anatomical structures in the eye is important towards the early detection of glaucoma and, therefore, potentially avoiding severe vision loss. In order to improve the segmentation of the optic disc and cup, we propose a novel self-supervised pre-training consisting in the multi-modal reconstruction of eye fundus images. This novel approach aims at facilitating the segmentation task and avoiding the necessity of excessively large annotated datasets. To validate the proposal, we perform several experiments on different public datasets. The results show that the proposed multi-modal self-supervised pre-training leads to a significant improvement in the performance of the segmentation task. Consequently, the presented approach shows remarkable potential towards further improving the interpretable and early diagnosis of a relevant disease as is glaucoma.