Deep Retinal Image Segmentation: A FCN-Based Architecture with Short and Long Skip Connections for Retinal Image Segmentation

This paper presents Deep Retinal Image Segmentation, a unified framework of retinal image analysis that provides both optic disc and exudates segmentation. The paper presents a new formulation of fully Convolutional Neural Networks (FCNs) that allows accurate segmentation of the retinal images. A major modification in these retinal image segmentation tasks are to improve and speed-up the FCNs training by adding short and long skip connections in standard FCNs architecture with class-balancing loss. The proposed method is experimented on the DRIONS-DB dataset for optic disc segmentation and the privately dataset for exudates segmentation, which achieves strong performance and significantly outperforms the-state-of-the-art. It achieves 93.12% sensitivity (Sen), 99.56% specificity (Spe), 89.90% Positive predictive value (PPV) and 90.93% F-score for optic disc segmentation while 81.35% Sen, 98.76% Spe, 81.64% PPV and 81.50% F-score for exudates segmentation respectively.