Advertisement

Fast and Robust Detection of Anatomical Landmarks Using Cascaded 3D Convolutional Networks Guided by Linear Square Regression

  • Zi-Rui Wang
  • Bao-Cai Yin
  • Jun Du
  • Cong Liu
  • Xiaodong Tao
  • Guoping Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

Detecting anatomical landmarks on structural magnetic resonance imaging (MRI) is an important medical computer-aid technique. However, for some brain anatomical landmarks detection, linear/non-linear registration with skull stripping across subjects is usually unavoidable. In this paper, we propose a novel method. Starting from the original MRI data, a series of 3D convolutional neural networks (cascaded 3D-CNNs) are adopted to iteratively update the predicted landmarks. Specially, the predicted landmarks of each 3D-CNN model are used to estimate the corresponding linear transformation matrix by linear square regression, which is very different from traditional registration methods. Based on the estimated matrix, we can use it to transform the original image for getting the new image for the next 3D-CNN model. With these cascaded 3D-CNNs and linear square regression, we can finally achieve registration and landmark detection.

Keywords

Anatomical landmark detection Cascaded 3D-CNNs Linear square regression Fast Robust 

Notes

Acknowledgments

This work was supported in part by the National Key R&D Program of China under contract No. 2017YFB1002202, in part by the National Natural Science Foundation of China under Grants 61671422 and U1613211, in part by the MOE-Microsoft Key Laboratory of USTC. The authors would like to thank Dr. Dinggang Shen for the contributions on implementation.

References

  1. 1.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar
  2. 2.
    Riegler, G., Urschler, M., Ruther, M., Bischof, H., Stern, D.: Anatomical landmark detection in medical applications driven by synthetic data. In: IEEE International Conference on Computer Vision Workshops, pp. 12–16 (2015)Google Scholar
  3. 3.
    Zheng, Y., Liu, D., Georgescu, B., Nguyen, H., Comaniciu, D.: 3D deep learning for efficient and robust landmark detection in volumetric data. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 565–572. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24553-9_69CrossRefGoogle Scholar
  4. 4.
    Payer, C., Štern, D., Bischof, H., Urschler, M.: Regressing heatmaps for multiple landmark localization using CNNs. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 230–238. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_27CrossRefGoogle Scholar
  5. 5.
    Ghesu, F.C., Georgescu, B., Mansi, T., Neumann, D., Hornegger, J., Comaniciu, D.: An artificial agent for anatomical landmark detection in medical images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 229–237. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46726-9_27CrossRefGoogle Scholar
  6. 6.
    Zhang, J., Gao, Y., Gao, Y., Munsell, B.C., Shen, D.: Detecting anatomical landmarks for fast Alzheimers disease diagnosis. IEEE Trans. Med. Imaging 35(12), 2524–2533 (2016)CrossRefGoogle Scholar
  7. 7.
    Zhang, J., Liu, M., Shen, D.: Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks. IEEE Trans. Image Process. 26(10), 4753–4764 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hyman, B.T., Van Hoesen, G.W., Damasio, A.R., Barnes, C.L.: Alzheimer’s disease: cell-specific pathology isolates the hippocampal formation. Science 225, 1168–1171 (1984)Google Scholar
  9. 9.
    Zhang, J., Liu, M., An, L., Gao, Y., Shen, D.: Alzheimer’s disease diagnosis using landmark-based features from longitudinal structural MR images. IEEE J. Biomed. Health Inform. 21(6), 1607–1616 (2017)Google Scholar
  10. 10.
    Jenkinson, M., Bannister, P., Michael, B., Stephen, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 17(2), 825–841 (2002)Google Scholar
  11. 11.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)Google Scholar
  12. 12.
    Chen, T., Mu, L., et al.: Mxnet: a flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 (2015)
  13. 13.
    Fischer, B., Modersitzki, J.: FLIRT: a flexible image registration toolbox. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds.) WBIR 2003. LNCS, vol. 2717, pp. 261–270. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-39701-4_28CrossRefzbMATHGoogle Scholar
  14. 14.
    Holmes, C.J., Hoge, R., Collins, L., Woods, R., Toga, A.W., Evans, A.C.: Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 22(2), 324–333 (1998)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zi-Rui Wang
    • 1
  • Bao-Cai Yin
    • 2
  • Jun Du
    • 1
  • Cong Liu
    • 2
  • Xiaodong Tao
    • 2
  • Guoping Hu
    • 2
  1. 1.NELSLIPUniversity of Science and Technology of ChinaHefeiChina
  2. 2.iFLYTEK AI ResearchHefeiChina

Personalised recommendations