Diffeomorphic Lung Registration Using Deep CNNs and Reinforced Learning

  • Jorge Onieva OnievaEmail author
  • Berta Marti-Fuster
  • María Pedrero de la Puente
  • Raúl San José Estépar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)


Image registration is a well-known problem in the field of medical imaging. In this paper, we focus on the registration of chest inspiratory and expiratory computed tomography (CT) scans from the same patient. Our method recovers the diffeomorphic elastic displacement vector field (DVF) by jointly regressing the direct and the inverse transformation. Our architecture is based on the RegNet network but we implement a reinforced learning strategy that can accommodate a large training dataset. Our results show that our method performs with a lower estimation error for the same number of epochs than the RegNet approach.


Deep learning Reinforced learning Lung registration Chest computed tomography Diffeomorphism 


  1. 1.
    Murphy, K., et al.: Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans. Med. Imaging 30(11), 1901–1920 (2011)CrossRefGoogle Scholar
  2. 2.
    Song, G., Tustison, N.J., Avants, B.B., Gee, J.C.: Lung CT image registration using diffeomorphic transformation models. In: Medical Image Analysis for the Clinic: A Grand Challenge, pp. 23–32 (2010)Google Scholar
  3. 3.
    Avants, B.B., Tustison, N., Song, G.: Advanced Normalization Tools (ANTS). Insight J. 2, 1–35 (2009)Google Scholar
  4. 4.
    Modat, M., McClelland, J., Ourselin, S.: Lung registration using the NiftyReg package. In: MICCAI2010 Workshop: Medical Image Analysis for the Clinic - A Grand Challenge, pp. 33–42 (2010)Google Scholar
  5. 5.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application tobreast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  6. 6.
    Rühaak, J., Heldmann, S., Kipshagen, T., Fischer, B.: Highly accurate fast lung CT registration. In: SPIE Medical Imaging 2013: Image Processing, vol. 8669, pp. 86690Y-1–86690Y-9 (2013)Google Scholar
  7. 7.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  8. 8.
    Miao, S., Wang, Z.J., Liao, R.: A CNN regression approach for real-time 2D/3D registration. IEEE Trans. Med. Imaging 35(5), 1352–1363 (2016)CrossRefGoogle Scholar
  9. 9.
    Eppenhof, K.A.J., Pluim, J.P.W.: Supervised local error estimation for nonlinear image registration using convolutional neural networks. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10133, February 2017Google Scholar
  10. 10.
    Sokooti, H., de Vos, B., Berendsen, F., Lelieveldt, B.P.F., Išgum, I., Staring, M.: Nonrigid image registration using multi-scale 3D convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 232–239. Springer, Cham (2017). Scholar
  11. 11.
    Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)CrossRefGoogle Scholar
  12. 12.
    Regan, E.a., et al.: Genetic epidemiology of COPD (COPDGene) study design. COPD 7(1), 32–43 (2010)CrossRefGoogle Scholar
  13. 13.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)CrossRefGoogle Scholar
  14. 14.
    Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst. 25, 2951–2959 (2012)Google Scholar
  15. 15.
    Castillo, R., et al.: A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive. Phys. Med. Biol. 58(9), 2861–2877 (2013)CrossRefGoogle Scholar
  16. 16.
    Ross, J.C., et al.: Lung extraction, lobe segmentation and hierarchical region assessment for quantitative analysis on high resolution computed tomography images. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 690–698. Springer, Heidelberg (2009). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Applied Chest Imaging Laboratory, Department of RadiologyBrigham and Women’s Hospital, Harvard Medical SchoolBostonUSA

Personalised recommendations