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3D U-net with Multi-level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images

  • Guodong Zeng
  • Xin Yang
  • Jing Li
  • Lequan Yu
  • Pheng-Ann Heng
  • Guoyan ZhengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10541)

Abstract

This paper addresses the problem of segmentation of proximal femur in 3D MR images. We propose a deeply supervised 3D U-net-like fully convolutional network for segmentation of proximal femur in 3D MR images. After training, our network can directly map a whole volumetric data to its volume-wise labels. Inspired by previous work, multi-level deep supervision is designed to alleviate the potential gradient vanishing problem during training. It is also used together with partial transfer learning to boost the training efficiency when only small set of labeled training data are available. The present method was validated on 20 3D MR images of femoroacetabular impingement patients. The experimental results demonstrate the efficacy of the present method.

Keywords

Deep learning Proximal femur MR images Segmentation 

Notes

Acknowledgment

This study was partially supported by the Swiss National Science Foundation via project 205321_163224/1.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Guodong Zeng
    • 1
  • Xin Yang
    • 2
  • Jing Li
    • 1
  • Lequan Yu
    • 2
  • Pheng-Ann Heng
    • 2
  • Guoyan Zheng
    • 1
    Email author
  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongSha TinHong Kong

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