Abstract
CT scanning has been widely used for diagnosis, staging and follow-up studies of pulmonary nodules, where image registration plays an essential role in follow-up assessment of CT images. However, it is challenging to align subtle structures in the lung CTs often with large deformation. Unsupervised learning-based registration methods, optimized according to the image similarity metrics, become popular in recent years due to their efficiency and robustness. In this work, we consider segmented tissues, i.e., airways, lobules, and pulmonary vessel structures, in a hierarchical way and propose a multi-stage registration workflow to predict deformation fields. The proposed workflow consists of two registration networks. The first network is the label alignment network, used to align the given segmentations. The second network is the vessel alignment network, used to further predict deformation fields to register vessels in lungs. By combining these two networks, we can register lung CT images not only in the semantic level but also in the texture level. In experiments, we evaluated the proposed algorithm on lung CT images for clinical follow-ups. The results indicate that our method has better performance especially in aligning critical structures such as airways and vessel branches in the lung, compared to the existing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394–424 (2018)
The National Lung Screening Trial Research Team: Reduced lung-cancer mortality with low-dose computed tomographic screening. New England J. Med. 365(5), 395–409 (2011)
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). https://doi.org/10.1007/978-3-319-66182-7_27
Eppenhof, K.A.J., Pluim, J.P.W.: Pulmonary CT registration through supervised learning with convolutional neural networks. IEEE Trans. Med. Imaging 38(5), 1097–1105 (2019)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsupervised learning model for deformable medical image registration. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9252–9260 (2018)
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems. pp. 2017–2025. Curran Associates, Inc. (2015)
Stergios, C., et al.: linear and deformable image registration with 3D convolutional neural networks. In: Stoyanov, D., et al. (eds.) RAMBO/BIA/TIA -2018. LNCS, vol. 11040, pp. 13–22. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00946-5_2
Hansen, L., Dittmer, D., Heinrich, M.P.: Learning deformable point set registration with regularized dynamic graph CNNs for large lung motion in COPD patients. In: Zhang, D., Zhou, L., Jie, B., Liu, M. (eds.) GLMI 2019. LNCS, vol. 11849, pp. 53–61. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35817-4_7
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). https://doi.org/10.1007/978-3-319-46723-8_49
Sato, Y., et al.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med. Image Anal. 2(2), 143–168 (1998)
Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54(3), 2033–2044 (2011)
Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.: elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)
Acknowledgement
This work was partially supported by the National Key Research and Development Program of China (2018YFC0116400).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, L. et al. (2020). Semantic Hierarchy Guided Registration Networks for Intra-subject Pulmonary CT Image Alignment. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-59716-0_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59715-3
Online ISBN: 978-3-030-59716-0
eBook Packages: Computer ScienceComputer Science (R0)
