Abstract
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.
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Acknowledgments
This research is partly supported by NSFC, China (No: 81600776), Committee of Science and Technology, Shanghai, China (No: 16411962100) and (No. 17JC1403000)
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Feng, Z., Yang, J., Yao, L., Qiao, Y., Yu, Q., Xu, X. (2017). Deep Retinal Image Segmentation: A FCN-Based Architecture with Short and Long Skip Connections for Retinal Image Segmentation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_76
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DOI: https://doi.org/10.1007/978-3-319-70093-9_76
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