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

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


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.


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



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.


  1. 1.
    Laborie L, Lehmann T, Engester I et al (2011) Prevalence of radiographic findings thought to be associated with femoroacetabular impingement in a population-based cohort of 2081 healthy young adults. Radiology 260:494–502CrossRefPubMedCentralGoogle Scholar
  2. 2.
    Leunig M, Beaulé P, Ganz R (2009) The concept of femoroacetabular impingement: current status and future perspectives. Clin Orthop Relat Res 467: 616–622CrossRefPubMedCentralGoogle Scholar
  3. 3.
    Clohisy J, Knaus E, Hunt DM et al (2009) Clinical presentation of patients with symptomatic anterior hip impingement. Clin Orthop Relat Res 467: 638–644CrossRefPubMedCentralGoogle Scholar
  4. 4.
    Perdikakis E, Karachalios T, Katonis P, Karantanas A (2011) Comparison of MR-arthrography and MDCT-arthrography for detection of labral and articular cartilage hip pathology. Skeletal Radiol 40:1441–1447CrossRefPubMedCentralGoogle Scholar
  5. 5.
    Xia Y, Fripp J, Chandra S, Schwarz R, Engstrom C, Crozier S (2013) Automated bone segmentation from large field of view 3D MR images of the hip joint. Phys Med Biol 21:7375–7390CrossRefGoogle Scholar
  6. 6.
    Xia Y, Chandra S, Engstrom C, Strudwick M, Crozier S, Fripp J (2014) Automatic hip cartilage segmentation from 3D MR images using arc-weighted graph searching. Phys Med Biol 59:7245–66CrossRefPubMedCentralGoogle Scholar
  7. 7.
    Gilles B, Magnenat-Thalmann N (2010) Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations. Med Image Anal 14:291–302CrossRefPubMedCentralGoogle Scholar
  8. 8.
    Arezoomand S, Lee WS, Rakhra K, Beaule P (2015) A 3D active model framework for segmentation of proximal femur in MR images. Int J CARS 10:55–66CrossRefGoogle Scholar
  9. 9.
    Chandra S, Xia Y, Engstrom C et al (2014) Focused shape models for hip joint segmentation in 3D magnetic resonance images. Med Image Anal 18: 567–578CrossRefPubMedCentralGoogle Scholar
  10. 10.
    Krizhevsky A, ISutskever, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., Red Hook, pp 1097–1105Google Scholar
  11. 11.
    Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR 2015), pp 3431–3440, BostonGoogle Scholar
  12. 12.
    Prasson A, Igel C, Petersen K et al (2013) Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: Proceedings of the 16th international conference on medical image computing and computer assisted intervention (MICCAI 2013), vol 16(Pt 2), pp 246–53, NagoyaGoogle Scholar
  13. 13.
    Cicek O, Abdulkadir A, Lienkamp S, Brox T, Ronneberger O (2016) 3D u-net: learning dense volumetric segmentation from sparse annotation. In: Proceedings of the 16th international conference on medical image computing and computer assisted intervention (MICCAI 2016). LNCS, vol 9901, pp 424–432, AthensGoogle Scholar
  14. 14.
    Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of the 2016 international conference on 3D vision (3DV). IEEE, pp 565–571, StanfordGoogle Scholar
  15. 15.
    Dou Q, Yu L, Chen H, Jin Y, Yang X, Qin J, Heng PA (2017) 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal 41:40–54CrossRefPubMedCentralGoogle Scholar
  16. 16.
    Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of international conference on machine learning (ICML 2015), LilleGoogle Scholar
  17. 17.
    Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Advances in neural information processing systems, pp 3320–3328, Curran Associates, Inc.Google Scholar
  18. 18.
    Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR 2009), Miami BeachGoogle Scholar
  19. 19.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556Google Scholar
  20. 20.
    Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR 2015), Boston. IEEE, pp 1–9Google Scholar
  21. 21.
    Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE international conference on computer vision (CVPR 2015), pp 4489–4497, BostonGoogle Scholar
  22. 22.
    Karasawa K, Oda M, Kitasakab T et al (2017) Multi-atlas pancreas segmentation: atlas selection based on vessel structure. Med Image Anal 39:18–28CrossRefPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

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

Personalised recommendations