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Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

  • Lequan Yu
  • Jie-Zhi Cheng
  • Qi Dou
  • Xin Yang
  • Hao Chen
  • Jing Qin
  • Pheng-Ann Heng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coefficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters.

Notes

Acknowledgments

The work described in this paper was supported by the grants from the Research Grants Council of the Hong Kong Special Administrative Region (Project No. CUHK 412513 and CUHK 14203115) and the National Natural Science Foundation of China (Project No. 61233012).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lequan Yu
    • 1
  • Jie-Zhi Cheng
    • 2
  • Qi Dou
    • 1
  • Xin Yang
    • 1
  • Hao Chen
    • 1
  • Jing Qin
    • 3
  • Pheng-Ann Heng
    • 1
    • 4
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatinHong Kong
  2. 2.Department of Electrical EngineeringChang Gung UniversityTaoyuanTaiwan
  3. 3.Centre for Smart Health, School of NursingThe Hong Kong Polytechnic UniversityKowloonHong Kong
  4. 4.Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina

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