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Deep Learning-Based Automatic Segmentation of the Proximal Femur from MR Images

  • Guodong Zeng
  • Guoyan Zheng
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1093)

Abstract

This chapter 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

MRI Segmentation Femoroacetabular impingement (FAI) Proximal femur Deep learning Fully Convolutional Network (FCN) Deep supervision 

Notes

Acknowledgements

This chapter was modified from the paper published by our group in the MICCAI 2017 Workshop on Machine Learning in Medical Imaging (Zeng and Zheng, MLMI@MICCAI 2017: 274-282). The related contents were reused with the permission. This study was partially supported by the Swiss National Science Foundation via project 205321_163224/1.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland

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