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

  • Zhongwei Feng
  • Jie Yang
  • Lixiu Yao
  • Yu Qiao
  • Qi Yu
  • Xun Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)


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.


Optic disc segmentation Exudates segmentation Convolutional neural network Skip connections Class-balancing loss 



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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zhongwei Feng
    • 1
  • Jie Yang
    • 1
  • Lixiu Yao
    • 1
  • Yu Qiao
    • 1
  • Qi Yu
    • 2
  • Xun Xu
    • 2
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai General HospitalShanghai Jiao Tong UniversityShanghaiChina

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