Abstract
We present a new unsupervised learning algorithm, “FAIM”, for 3D medical image registration. With a different architecture than the popular “U-net” [10], the network takes a pair of full image volumes and predicts the displacement fields needed to register source to target. Compared with “U-net” based registration networks such as VoxelMorph [2], FAIM has fewer trainable parameters but can achieve higher registration accuracy as judged by Dice score on region labels in the Mindboggle-101 dataset. Moreover, with the proposed penalty loss on negative Jacobian determinants, FAIM produces deformations with many fewer “foldings”, i.e. regions of non-invertibility where the surface folds over itself. We varied the strength of this penalty and found that FAIM is able to maintain both the advantages of higher accuracy and fewer “folding” locations over VoxelMorph, over a range of hyper-parameters. We also evaluated Probabilistic VoxelMorph [3], both in its original form and with its U-net backbone replaced with our FAIM network. We found that the choice of backbone makes little difference. The original version of FAIM outperformed Probabilistic VoxelMorph for registration accuracy, and also for invertibility if FAIM is trained using an anti-folding penalty. Code for this paper is freely available at https://github.com/dykuang/Medical-image-registration.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Their corresponding labels are not used in training.
References
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)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9252–9260 (2018)
Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning for fast probabilistic diffeomorphic registration. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018, Part I. LNCS, vol. 11070, pp. 729–738. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_82
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint: arXiv:1412.6980 (2014)
Klein, A., Tourville, J.: 101 labeled brain images and a consistent human cortical labeling protocol. Front. Neurosci. 6, 171 (2012)
Li, H., Fan, Y.: Non-rigid image registration using fully convolutional networks with deep self-supervision. arXiv preprint: arXiv:1709.00799 (2017)
Rohé, M.-M., Datar, M., Heimann, T., Sermesant, M., Pennec, X.: SVF-Net: learning deformable image registration using shape matching. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017, Part I. LNCS, vol. 10433, pp. 266–274. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_31
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shan, S., et al.: Unsupervised end-to-end learning for deformable medical image registration. arXiv preprint: arXiv:1711.08608 (2017)
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, Part I. LNCS, vol. 10433, pp. 232–239. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_27
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Wang, S., Kim, M., Wu, G., Shen, D.: Scalable high performance image registration framework by unsupervised deep feature representations learning. In: Deep Learning for Medical Image Analysis, pp. 245–269. Elsevier (2017)
Yang, X., Kwitt, R., Niethammer, M.: Fast predictive image registration. In: Carneiro, G., et al. (eds.) LABELS 2016/DLMIA 2016. LNCS, vol. 10008, pp. 48–57. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_6
Zhang, J.: Inverse-consistent deep networks for unsupervised deformable image registration. arXiv preprint: arXiv:1809.03443 (2018)
Acknowledgements
This work was supported in part by a Discovery Grant from NSERC Canada.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kuang, D., Schmah, T. (2019). FAIM – A ConvNet Method for Unsupervised 3D Medical Image Registration. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_74
Download citation
DOI: https://doi.org/10.1007/978-3-030-32692-0_74
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32691-3
Online ISBN: 978-3-030-32692-0
eBook Packages: Computer ScienceComputer Science (R0)