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)


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.


Deep learning Proximal femur MR images Segmentation 



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


  1. 1.
    Laborie, L., Lehmann, T., Engesæter, I., et al.: Prevalence of radiographic findings thought to be associated with femoroacetabular impingement in a population-based cohort of 2081 healthy young adults. Radiology 260, 494–502 (2011)CrossRefGoogle Scholar
  2. 2.
    Leunig, M., Beaulé, P., Ganz, R.: The concept of femoroacetabular impingement: current status and future perspectives. Clin. Orthop. Relat. Res. 467, 616–622 (2009)CrossRefGoogle Scholar
  3. 3.
    Clohisy, J., Knaus, E., Hunt, D.M., et al.: Clinical presentation of patients with symptomatic anterior hip impingement. Clin. Orthop. Relat. Res. 467, 638–644 (2009)CrossRefGoogle Scholar
  4. 4.
    Perdikakis, E., Karachalios, T., Katonis, P., Karantanas, A.: Comparison of MR-arthrography and MDCT-arthrography for detection of labral and articular cartilage hip pathology. Skeletal Radiol. 40, 1441–1447 (2011)CrossRefGoogle Scholar
  5. 5.
    Xia, Y., Fripp, J., Chandra, S., Schwarz, R., Engstrom, C., Crozier, S.: Automated bone segmentation from large field of view 3D MR images of the hip joint. Phys. Med. Biol. 21, 7375–7390 (2013)CrossRefGoogle Scholar
  6. 6.
    Xia, Y., Chandra, S., Engstrom, C., Strudwick, M., Crozier, S., Fripp, J.: Automatic hip cartilage segmentation from 3D MR images using arc-weighted graph searching. Phys. Med. Biol. 59, 7245–66 (2014)CrossRefGoogle Scholar
  7. 7.
    Gilles, B., Magnenat-Thalmann, N.: Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations. Med. Image Anal. 14, 291–302 (2010)CrossRefGoogle Scholar
  8. 8.
    Arezoomand, S., Lee, W.S., Rakhra, K., Beaule, P.: A 3D active model framework for segmentation of proximal femur in MR images. Int. J. CARS 10, 55–66 (2015)CrossRefGoogle Scholar
  9. 9.
    Chandra, S., Xia, Y., Engstrom, C., et al.: Focused shape models for hip joint segmentation in 3D magnetic resonance images. Med. Image Anal. 18, 567–578 (2014)CrossRefGoogle Scholar
  10. 10.
    Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012)Google Scholar
  11. 11.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)Google Scholar
  12. 12.
    Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., Nielsen, M.: Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 246–253. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40763-5_31 CrossRefGoogle Scholar
  13. 13.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). doi: 10.1007/978-3-319-46723-8_49 CrossRefGoogle Scholar
  14. 14.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of 2016 International Conferece on 3D Vision (3DV), pp. 565–571. IEEE (2016)Google Scholar
  15. 15.
    Dou, Q., Yu, L., Chen, H., Jin, Y., Yang, X., Qin, J., Heng, P.A.: 3D deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41, 40–54 (2017)CrossRefGoogle Scholar
  16. 16.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of ICML (2015)Google Scholar
  17. 17.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)Google Scholar
  18. 18.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009 (2009)Google Scholar
  19. 19.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR (2014)Google Scholar
  20. 20.
    Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: CVPR 2015, pp. 1–9. IEEE (2015)Google Scholar
  21. 21.
    Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision (CVPR), pp. 4489–4497 (2015)Google Scholar
  22. 22.
    Karasawa, K., Oda, M., Kitasakab, T., et al.: Multi-atlas pancreas segmentation: Atlas selection based on vessel structure. Med. Image Anal. 39, 18–28 (2017)CrossRefGoogle Scholar

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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