RIANet: Recurrent interleaved attention network for cardiac MRI segmentation.

Journal: Computers in biology and medicine
Published Date: May 7, 2019

Abstract

BACKGROUND: Segmentation of anatomical structures of the heart from cardiac magnetic resonance images (MRI) has a significant impact on the quantitative analysis of the cardiac contractile function. Although deep convolutional neural networks (ConvNets) have achieved considerable success in medical imaging segmentation, it is still a challenging task for existing deep ConvNets to precisely and automatically segment multiple heart structures from cardiac MRI. This paper presents a novel recurrent interleaved attention network (RIANet) to comprehensively tackle this issue.

Authors

  • Qianqian Tong
    School of Computer Science, Wuhan University, Wuhan, 430072, China; Guangdong Provincial Key Laboratory of Machine Vision and Virtual Reality Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Caizi Li
    School of Computer Science, Wuhan University, Wuhan, 430072, China.
  • Weixin Si
    Guangdong Provincial Key Laboratory of Machine Vision and Virtual Reality Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Xiangyun Liao
    Guangdong Provincial Key Laboratory of Machine Vision and Virtual Reality Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. Electronic address: xyunliao@gmail.com.
  • Yaliang Tong
    Department of Cardiology China-Japan Union Hospital of Jilin University, Changchun, 130000, China.
  • Zhiyong Yuan
    School of Computer Science, Wuhan University, Wuhan, 430072, China.
  • Pheng Ann Heng

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