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DeepDisc: Optic Disc Segmentation Based on Atrous Convolution and Spatial Pyramid Pooling

  • Zaiwang GuEmail author
  • Peng Liu
  • Kang Zhou
  • Yuming Jiang
  • Haoyu Mao
  • Jun Cheng
  • Jiang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11039)

Abstract

The optic disc (OD) segmentation is an important step for fundus image base disease diagnosis. In this paper, we propose a novel and effective method called DeepDisc to segment the OD. It mainly contains two components: atrous convolution and spatial pyramid pooling. The atrous convolution adjusts filter’s field-of-view and controls the resolution of features. In addition, the spatial pyramid pooling module probes convolutional features at multiple scales and encodes global context information. Both of them are used to further boost OD segmentation performance. Finally, we demonstrate that our DeepDisc system achieves state-of-the-art disc segmentation performance on the ORIGA and Messidor datasets without any post-processing strategies, such as dense conditional random field.

Keywords

Disc segmentation Atrous convolution Spatial pyramid pooling 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zaiwang Gu
    • 1
    • 2
    Email author
  • Peng Liu
    • 1
    • 3
  • Kang Zhou
    • 4
  • Yuming Jiang
    • 1
    • 3
  • Haoyu Mao
    • 1
  • Jun Cheng
    • 1
  • Jiang Liu
    • 1
  1. 1.Cixi Institute of Biomedical EngineeringNingbo Institute of Materials Technology and Engineering, Chinese Academy of SciencesBeijingChina
  2. 2.School of Mechatronic Engineering and AutomationShanghai UniversityShanghaiChina
  3. 3.University of Electronic Science and Technology of ChinaChengduChina
  4. 4.School of Information Science and TechnologyShanghaiTech UniversityShanghaiChina

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